Mokka Guide to Choosing an AI Recruiting Partner

Part I: The Mokka Advantage

Hiring today is harder than ever. Every open role gets flooded with applications, and it's nearly impossible to tell who's a great fit and who just has a resume that's been perfectly optimized with AI to match your job description. Traditional tools that just match keywords are easily fooled, leaving your team to sort through a mountain of applications, hoping to find a few hidden gems.

Mokka was built to fix this. We believe the secret to better hiring isn't just about screening resumes faster; it's about getting better, more reliable information from the very beginning. Instead of just analyzing a document that's often exaggerated, Mokka generates a new, richer set of evidence directly from your candidates.

Here's how we provide a smarter, fairer, and more trustworthy way to hire.

1. A Complete and Trustworthy Picture of Every Candidate

A resume only tells part of the story, and often, it's the part the candidate wants you to see. Mokka goes beyond the resume to give you a complete, 360-degree view of every applicant, grounded in facts.

• We Enrich, Not Just Review: Mokka starts with the resume but doesn't stop there. We enrich every profile with third-party data and, most importantly, with insights from our automated pre-screening interview. This turns a flat document—which can be easily over-optimized—into a rich, evidence-based profile.
• Built-in Integrity Checks: In an age of AI-generated applications, trust is essential. Mokka's Answer and Profile Integrity analytics act as a trust layer for your hiring process. We automatically check for suspicious patterns, like mismatches between a candidate's resume, their LinkedIn profile, and the answers they provide, flagging potential risks so you can hire with confidence.

2. A Screening Process Candidates Actually Appreciate

Tough screening and a great candidate experience shouldn't be mutually exclusive. We've designed a process that is both rigorous for you and respectful for them, which is why candidates give Mokka a 4.7/5 rating.

• Flexibility and Choice: Candidates can share their experience how they feel most comfortable—using text, voice, or video. With privacy features like the ability to hide their self-view and no stressful time limits, they can put their best foot forward.
• We Help Candidates Shine: Our pre-screening interview is designed to be a supportive conversation, not an interrogation. Mokka provides real-time tips and prompts candidates to provide more complete answers, just like a great recruiter would. This helps you get the reliable data you need for a fair evaluation, avoiding the "garbage in, garbage out" problem of simple chatbots.
• Candidates Feel Seen, Not Processed: We ask about accomplishments both inside and outside of work, because great talent comes from everywhere. This approach makes candidates feel valued as individuals. The proof is in the numbers: 40-90% of candidates complete the process, a stark contrast to the 90%+ dropout rate seen with traditional skills tests.

3. Powerful Screening That's Incredibly Easy to Use

Your team is busy. Mokka is designed to deliver powerful results without adding complexity to your workflow.

• Smarter Intake, Better Screening: A great hire starts with understanding what really matters. Mokka's intake process captures detailed input from your recruiters on the importance of various job requirements—it doesn't just scan a job description for keywords. The recruiter reviews and signs off on these requirements, ensuring the screening is aligned with your team's real priorities from day one.
• Works Off-the-Shelf in 5 Minutes: You can be up and running in minutes. Mokka works out-of-the-box and only requires a quick recruiter review to get started. It's not a heavy, complex assessment tool that requires weeks of setup.
• Fully Customizable When You Need It: For more specific needs, you can easily generate custom, structured interviews with objective scoring rubrics to ensure every evaluation is consistent and fair.

4. Deep ATS Integration to Avoid Tool Bloat

Your Applicant Tracking System (ATS) is your system of record, and we keep it that way. Mokka is not another siloed tool that creates more work. It's a powerful screening layer that integrates deeply with your existing ATS.

• A Seamless Workflow: Data flows effortlessly between Mokka and your ATS. Every decision made in Mokka is reflected in the ATS, and vice versa.
• Intelligence Where You Need It: Mokka is optimized for top-of-funnel screening, allowing you to easily stack-rank hundreds of candidates. Rich Mokka candidate profiles are posted directly into your ATS for easy access by recruiters and hiring managers, keeping everything in one place.

5. Predictable Pricing That Scales with You

Most AI screening tools, especially for video interviews, use per-application or per-interview pricing. This creates unpredictable costs and a frustrating workflow where your team has to manually review every single applicant just to decide who to invite into the tool.

Mokka is different. We offer simple, seat-based pricing that includes unlimited job requisitions and unlimited applications. This gives you predictable costs and the freedom to screen 100% of your talent pipeline automatically, without worrying about a surprise bill.

Part II: Working with Your ATS

Mokka is designed to work with your ATS, not replace it. Your ATS is your system of record for managing candidates, workflows, and hiring pipelines. Mokka enhances that investment by adding the deep screening intelligence that workflow management systems—even those with AI features—cannot provide on their own.

Think of it this way: your ATS manages where candidates go. Mokka determines who should get there and why—providing evidence-based pre-screening, accomplishment verification, and integrity checks that populate your ATS with rich, trustworthy candidate profiles.

Whether you use Ashby, Greenhouse, Lever, Workable, or any other ATS, Mokka integrates seamlessly to give you best-of-breed screening depth while keeping your existing workflow system intact.

Mokka vs. Your ATS's Built-in Screening (e.g., Ashby, Workable)

A Quick Look at ATS Screeners:

Many modern Applicant Tracking Systems (ATS) now include their own AI-powered screening features. They use AI to analyze resumes and match them against the job description, helping recruiters sort through applicants more quickly.

The Mokka Difference:

We love a good ATS—in fact, we partner and integrate with them. While their built-in screening is a good first step, it's fundamentally limited because it only analyzes one thing: the resume. In today's world, where resumes are overly optimized to match job descriptions, this provides a very limited signal. Mokka complements your ATS by adding the depth it lacks.

• Goes Beyond the Resume: We conduct pre-screening interviews to gather evidence of actual accomplishments that you can't find in a resume.
• Verifies Information: Our Profile and Answer Integrity checks spot suspicious patterns that a simple resume-reader can't.
• Captures Recruiter Insight: Our screening is based on a comprehensive recruiter intake, not just a keyword match against the job description. Mokka turns your ATS from a simple filing cabinet into a powerful, evidence-based decision engine.

Key Questions to Consider:

• How does the tool get beyond the resume to validate a candidate's actual experience and accomplishments?
• What mechanisms are in place to check for integrity issues, like major inconsistencies between a resume and a LinkedIn profile, or signs of an AI-generated application?
• How does the screening process allow your hiring team to easily adjust and prioritize what's most important for a role, ensuring the evaluation aligns with their real-world needs?

Working with BreezyHR

A Quick Look at BreezyHR:

BreezyHR is an all-in-one recruiting platform for small to mid-sized businesses, offering job advertising across 50+ sites, automated screening and qualification workflows, and interview scheduling. The platform features "Breezy Intelligence," an AI capability for candidate evaluation, along with resume parsing, pre-screening automation, and HRIS integrations.

The Mokka Difference:

BreezyHR manages your recruiting workflow efficiently. Mokka integrates with BreezyHR to add specialized screening depth that all-in-one platforms cannot match.

• Mokka Complements BreezyHR: We enhance BreezyHR's workflow automation. BreezyHR handles job distribution, scheduling, and candidate communication. Mokka provides the evidence-based screening intelligence—pre-screening interviews, accomplishment verification, and integrity checks—that determine which candidates deserve that investment.
• Workflow Automation vs. Evidence Generation: BreezyHR automates tedious recruitment tasks like posting jobs and scheduling interviews. Mokka generates new evidence through structured pre-screening interviews—collecting specific accomplishment examples and measurable outcomes that don't exist on any resume.
• Resume Parsing vs. Multi-Source Verification: BreezyHR's resume parsing extracts information from applications. Mokka cross-checks every pre-screening response against resumes, LinkedIn profiles, and third-party data through our Profile and Answer Integrity analytics—detecting AI-generated applications and inconsistencies.
• Pre-Screening Automation vs. Integrity-Verified Screening: BreezyHR can automate basic pre-screening questions in application forms. Mokka conducts comprehensive pre-screening interviews after application, with integrity verification that ensures responses are authentic and consistent across multiple sources.
• All-in-One Platform vs. Best-of-Breed Screening: BreezyHR provides broad feature coverage across sourcing, workflow, and basic screening. Mokka specializes exclusively in evidence-based screening depth—purpose-built for accomplishment verification and integrity analytics that all-in-one platforms cannot match.

Key Questions to Consider:

• When Breezy Intelligence evaluates candidates, how does it verify accomplishments beyond analyzing resume content?
• What pre-screening happens before candidates enter your BreezyHR pipeline—basic application questions, or comprehensive evidence-based evaluation?
• How do you detect when candidates use AI to optimize their application responses?
• Can you integrate a dedicated screening platform like Mokka with BreezyHR to get specialized depth while keeping BreezyHR's workflow automation?

Working with iCIMS

A Quick Look at iCIMS:

iCIMS positions itself as "the leading enterprise recruiting software and applicant tracking system" for large organizations. The platform helps employers "attract, engage, hire, and advance today's top talent" through enterprise-scale workflow management, integrations, and recruiting automation designed for high-volume, multi-location hiring operations.

The Mokka Difference:

iCIMS excels at enterprise recruiting workflow management at scale. Mokka integrates with iCIMS to add the evidence-based screening depth that enterprise workflow platforms need to handle high application volumes effectively.

• Mokka Complements iCIMS: We work seamlessly with iCIMS customers managing complex, high-volume recruiting operations. iCIMS is your enterprise system of record for managing candidates across multiple locations and business units. Mokka provides the screening intelligence that determines which candidates enter those pipelines—with evidence-based evaluation that scales with your hiring needs.
• Enterprise Workflow vs. Evidence-Based Screening: iCIMS excels at managing recruiting workflows, compliance, and integrations across large organizations. Mokka excels at pre-screening interviews that generate new evidence—collecting accomplishment examples, measurable outcomes, and verifiable claims that don't exist in application data.
• High-Volume Processing vs. Quality Verification: Enterprise ATS platforms prioritize processing large candidate volumes efficiently. Mokka ensures quality at scale—cross-checking every pre-screening response against resumes, LinkedIn profiles, and third-party data through our Profile and Answer Integrity analytics, preventing low-quality candidates from consuming recruiter time.
• System of Record vs. Screening Intelligence: iCIMS is the perfect system of record for enterprise recruiting operations. Mokka provides the screening depth that populates iCIMS with rich, evidence-based candidate profiles—giving hiring managers reviewable evidence at scale, not just application data.
• Resume Analysis vs. Multi-Source Verification: Enterprise ATS platforms primarily analyze resume content and application data at scale. Mokka cross-checks every pre-screening response against multiple sources, detecting AI-generated applications and inconsistencies before candidates reach busy enterprise recruiters.

Key Questions to Consider:

• For high-volume roles receiving 500+ applications, how do you screen candidates deeply before they consume valuable recruiter time?
• When processing thousands of applications monthly, how do you verify candidate accomplishments beyond analyzing resume keywords?
• What mechanisms detect AI-generated applications and integrity issues at enterprise scale?
• Can you integrate a dedicated screening platform like Mokka with iCIMS to add evidence-based depth while keeping iCIMS's enterprise workflow management?

Working with Pinpoint

A Quick Look at Pinpoint:

Pinpoint is a comprehensive ATS organizing hiring across five stages: Plan, Attract, Engage, Select, and Onboard. The platform offers AI features including an AI Chatbot for candidate engagement, AI Custom Fields for data collection, and Blind Recruitment tools for bias reduction. Pinpoint emphasizes bringing "every part of the selection process into the ATS for better decisions, faster" with automation capabilities, sourcing extensions, and talent pool management.

The Mokka Difference:

Pinpoint consolidates your recruiting workflow into one unified system. Mokka integrates with Pinpoint to add the screening intelligence layer that unified systems need to make those "better decisions, faster."

• Mokka Complements Pinpoint: We work well with Pinpoint customers who want to add screening depth to their unified recruiting platform. Pinpoint manages your end-to-end hiring workflow across all stages. Mokka provides the evidence-based screening that populates Pinpoint with verified candidate data—determining which candidates advance through those carefully designed stages.
• Unified Workflow vs. Evidence Generation: Pinpoint brings every recruiting tool into one platform for consistency and efficiency. Mokka generates new evidence through structured pre-screening interviews—collecting specific accomplishments, measurable outcomes, and verifiable examples that don't exist in application forms or chatbot interactions.
• AI Chatbot Engagement vs. AI Pre-Screening Interviews: Pinpoint's AI Chatbot handles candidate engagement and basic Q&A. Mokka's AI conducts comprehensive pre-screening interviews—collecting accomplishment evidence, probing for details, and running integrity checks that chatbots cannot provide.
• Blind Recruitment Features vs. Integrity Verification: Pinpoint offers blind recruitment tools to reduce bias in selection. Mokka adds integrity verification—cross-checking every response against resumes, LinkedIn profiles, and third-party data to ensure the candidates in your pipeline are genuinely qualified, regardless of how they're screened.
• Talent Pool Management vs. Applicant Verification: Pinpoint helps you build and manage talent pools for future opportunities. Mokka helps you verify which applicants deserve to be in those pools—through pre-screening interviews that collect reviewable evidence of accomplishments and detect AI-generated applications.

Key Questions to Consider:

• When bringing "every part of the selection process into the ATS," how deeply does that selection verify candidate accomplishments beyond application data?
• How does your AI Chatbot's candidate engagement translate into verified, evidence-based screening?
• What happens before candidates enter your talent pool—are they verified through comprehensive pre-screening?
• Can you integrate a specialized screening platform like Mokka with Pinpoint to add evidence-based depth while keeping Pinpoint's unified workflow?

Working with SmartRecruiters

A Quick Look at SmartRecruiters:

SmartRecruiters offers an integrated recruitment platform (SmartOS) spanning the full hiring lifecycle with "Winston Intelligence," an AI-powered layer that "anticipates your needs, reduces admin work." The platform features AI-powered candidate screening, AI talent matching, dynamic scheduling (reducing scheduling work by 97%), conversational AI chat, recruitment CRM, and analytics. SmartRecruiters claims "70% reduction in time-to-hire" and "50% increase in hiring velocity."

The Mokka Difference:

SmartRecruiters optimizes hiring velocity through AI-powered automation. Mokka integrates with SmartRecruiters to ensure that velocity doesn't come at the cost of quality—adding evidence-based screening depth that workflow automation cannot provide.

• Mokka Complements SmartRecruiters: We work well with SmartRecruiters customers who want both speed and quality. SmartRecruiters' Winston Intelligence handles workflow automation, scheduling, and candidate communication. Mokka's intelligence handles screening depth—conducting pre-screening interviews, verifying accomplishments, and running integrity checks that ensure the candidates moving through SmartRecruiters' fast workflows are genuinely qualified.
• Hiring Velocity vs. Hiring Quality: SmartRecruiters emphasizes "50% increase in hiring velocity" through automation. Mokka ensures quality keeps pace with that velocity—cross-checking every pre-screening response against resumes, LinkedIn profiles, and third-party data to verify candidates are worth the accelerated process.
• AI Talent Matching vs. Evidence-Based Evaluation: SmartRecruiters' AI matches candidates to roles based on resume analysis and profile data. Mokka generates new evidence through structured pre-screening interviews—collecting specific examples, measurable outcomes, and verifiable accomplishments that matching algorithms cannot access.
• Conversational AI Chat vs. Structured Pre-Screening: SmartRecruiters offers conversational AI for candidate engagement. Mokka conducts structured pre-screening interviews with integrity verification—ensuring responses are authentic, consistent across sources, and provide reviewable evidence for hiring decisions.
• Workflow Automation vs. Screening Intelligence: Winston Intelligence reduces administrative work through automation. Mokka provides screening intelligence through evidence generation—automating the data collection that determines which candidates deserve to move through SmartRecruiters' optimized workflows.

Key Questions to Consider:

• When reducing time-to-hire by 70%, how do you ensure screening quality doesn't decline?
• How does AI talent matching verify that top-matched candidates actually accomplished what their profiles claim?
• What happens before candidates enter your accelerated hiring workflow—basic resume screening, or comprehensive evidence-based pre-screening?
• For high-velocity hiring, how do you detect AI-generated applications and integrity issues without slowing down?
• Can you integrate a screening depth platform like Mokka with SmartRecruiters to maintain quality while achieving velocity goals?

Working with Kula

A Quick Look at Kula:

Kula positions itself as a "Gen 3 AI-Native ATS" that combines recruiting workflow management with AI-powered candidate screening and sourcing. The platform offers AI scoring and ranking based on job descriptions, contextual resume parsing, automated scheduling, an AI notetaker for interviews, and candidate sourcing capabilities. Kula emphasizes being "built for AI from the ground up" rather than adding AI features to legacy ATS architecture.

The Mokka Difference:

Kula is an all-in-one ATS with AI screening features built in. Mokka complements and integrates with your existing ATS (including Kula) to provide the deep screening intelligence that workflow management systems cannot deliver on their own.

• ATS with AI Features vs. Dedicated Screening Depth: Kula manages your recruiting workflow and adds AI resume scoring. Mokka specializes exclusively in evidence-based screening—conducting pre-screening interviews, collecting accomplishment evidence, and running integrity checks that go far beyond what an ATS's built-in features can provide.
• Resume Scoring vs. Evidence Generation: Kula's AI scoring ranks candidates based on resume content matched against job descriptions. Mokka generates new evidence through structured pre-screening interviews—collecting specific accomplishments, measurable outcomes, and verifiable examples that don't exist on any resume.
• Contextual Parsing vs. Multi-Source Verification: Kula parses resumes with contextual understanding to extract information. Mokka cross-checks every candidate response against their resume, LinkedIn profile, and third-party data through our Profile and Answer Integrity analytics—detecting inconsistencies and AI-generated applications.
• System of Record vs. Screening Intelligence: Kula excels at being your recruiting system of record—managing pipeline stages, scheduling, notes, and workflow. Mokka excels at generating the screening intelligence that determines who moves through that pipeline—providing rich, evidence-based candidate profiles that integrate directly into your ATS.
• AI Notetaker vs. AI Pre-Screener: Kula's AI notetaker captures insights from recruiter-led interviews. Mokka's AI conducts the pre-screening interview itself—automating the tedious data collection phase so recruiters only invest time in candidates backed by verified evidence.
• Built-in Features vs. Best-of-Breed Integration: Kula offers an integrated suite of features within a single ATS platform. Mokka integrates with any ATS (including Kula) as a specialized screening layer—giving you best-in-class screening depth without vendor lock-in if you ever switch ATS systems.

Key Questions to Consider:

• How does your ATS's AI scoring verify that candidates actually accomplished what they claim on their resumes beyond keyword matching?
• When candidates optimize resumes with AI to match job descriptions perfectly, how do you differentiate genuine qualifications from well-optimized documents?
• What mechanisms detect inconsistencies between a candidate's resume and their LinkedIn profile, or identify AI-generated application responses?
• How deeply can your ATS screen candidates before you invest valuable recruiter time in interviews—resume analysis only, or evidence-based pre-screening?
• If you decide to switch ATS systems in the future, what happens to your screening capabilities and historical candidate intelligence?

Working with Ashby

A Quick Look at Ashby:

Ashby is a modern, all-in-one ATS platform for fast-growing companies, offering recruiting workflow management, analytics, scheduling, and AI-powered features. Ashby markets AI as "embedded in every layer" of their platform, with capabilities like an AI Notetaker (in beta) for interview transcription and AI-assisted analytics. The platform emphasizes end-to-end recruiting automation, reporting depth, and integration ecosystem for managing your entire hiring process.

The Mokka Difference:

Ashby is an excellent ATS that manages your recruiting workflow. Mokka integrates with Ashby to add the deep screening intelligence that workflow management systems—even those with AI features—cannot provide on their own.

• Mokka Complements Ashby: We love working with Ashby customers. Ashby is your system of record for managing candidates through your pipeline. Mokka is your screening intelligence layer that determines who enters that pipeline and why—providing evidence-based evaluation that goes far beyond resume analysis.
• Workflow Management vs. Evidence-Based Screening: Ashby excels at managing recruiting workflows, analytics, scheduling, and pipeline stages. Mokka excels at pre-screening interviews that generate new evidence—collecting accomplishment examples, measurable outcomes, and verifiable claims that don't exist on resumes or in Ashby's database.
• AI Notetaker vs. AI Pre-Screener: Ashby's AI Notetaker captures insights from recruiter-led interviews. Mokka's AI conducts the pre-screening interview itself before any recruiter involvement—automating the tedious data collection phase so your team only invests time in candidates backed by verified evidence.
• Resume Analysis vs. Multi-Source Verification: Even with AI features, ATS platforms primarily analyze resume content and application data. Mokka cross-checks every pre-screening response against resumes, LinkedIn profiles, and third-party sources through our Profile and Answer Integrity analytics—detecting AI-generated applications and inconsistencies.
• System of Record vs. Screening Depth: Ashby is the perfect system of record for your recruiting operations. Mokka provides the screening depth that populates Ashby with rich, evidence-based candidate profiles—giving hiring managers reviewable evidence beyond "resume looked good."
• Built-in AI Features vs. Dedicated Screening Platform: Ashby adds helpful AI features to workflow management. Mokka is purpose-built exclusively for evidence-based screening—with structured interviews, accomplishment probing, integrity verification, and recruiter-controlled evaluation criteria that ATS platforms cannot match.

Key Questions to Consider:

• How does your ATS's AI verify that candidates actually accomplished what they claim on resumes beyond analyzing keywords and formatting?
• When candidates use AI to optimize resumes perfectly for your job descriptions, how do you differentiate genuine qualifications from well-crafted documents?
• What pre-screening happens before candidates enter your ATS pipeline—are you manually reviewing every resume, or do you have automated, evidence-based evaluation?
• How deeply does your current screening process verify accomplishments, check for integrity issues, or enrich candidate profiles before recruiters invest time?
• Can you integrate a dedicated screening platform like Mokka with Ashby to get best-of-breed screening depth while keeping Ashby as your workflow system of record?

Working with Greenhouse

A Quick Look at Greenhouse:

Greenhouse is one of the leading ATS platforms for structured hiring, offering end-to-end recruiting workflow management, interview kits, scorecards, and analytics. The platform includes AI-powered features like "instant candidate summaries," AI-powered job boards, and interview plan generation. Greenhouse emphasizes structured hiring methodology, DE&I tools, and an extensive integration ecosystem with hundreds of recruiting partners.

The Mokka Difference:

Greenhouse is a best-in-class ATS for managing structured hiring workflows. Mokka integrates with Greenhouse to provide the evidence-based screening depth that workflow platforms—even with AI summaries—cannot deliver on their own.

• Mokka Complements Greenhouse: We partner with Greenhouse customers frequently. Greenhouse manages your hiring structure, scorecards, and candidate progression through stages. Mokka generates the screening intelligence that determines who advances through those stages—providing pre-screening interview evidence, accomplishment verification, and integrity checks.
• Candidate Summaries vs. Evidence Generation: Greenhouse's AI provides "instant candidate summaries" from resume content. Mokka generates new evidence through structured pre-screening interviews—collecting specific examples, measurable outcomes, and verifiable accomplishments that no resume summary can provide.
• Interview Kits vs. Pre-Screening Interviews: Greenhouse helps your team conduct structured interviews with standardized questions and scorecards. Mokka automates the pre-screening interview that happens before candidates reach your team—collecting evidence upfront so recruiters only interview candidates with verified qualifications.
• Resume-Based Screening vs. Multi-Source Verification: Greenhouse analyzes application data and resumes to create summaries. Mokka cross-checks every pre-screening response against resumes, LinkedIn profiles, and third-party data through our Profile and Answer Integrity analytics—detecting AI-generated content and inconsistent claims.
• System of Record vs. Screening Intelligence: Greenhouse is your hiring system of record with structured methodology and compliance tools. Mokka is your screening intelligence layer that populates Greenhouse with rich, evidence-based candidate profiles—ensuring the candidates in your pipeline are backed by verifiable data, not just polished resumes.
• Structured Hiring Methodology vs. Evidence-Based Pre-Screening: Greenhouse provides structure for your hiring process through scorecards and interview kits. Mokka provides evidence for your hiring decisions through pre-screening interviews, accomplishment probing, and integrity verification—ensuring the candidates who enter Greenhouse's structured process are genuinely qualified.

Key Questions to Consider:

• When AI generates "instant candidate summaries" from resumes, how do you verify candidates actually accomplished what those summaries claim?
• What happens before candidates enter your Greenhouse pipeline—manual resume review, or automated evidence-based screening?
• How do you detect when candidates use AI to optimize their resumes and application responses to match your job descriptions perfectly?
• Can you cross-check candidate claims against LinkedIn profiles, resume inconsistencies, or third-party data before they reach interview stages?
• Would integrating a dedicated screening platform like Mokka with Greenhouse give you best-of-breed pre-screening while keeping Greenhouse as your structured hiring system of record?

Working with Lever

A Quick Look at Lever:

Lever is a comprehensive talent relationship management platform combining ATS functionality with CRM capabilities for relationship-building with candidates. The platform offers recruiting automation, candidate nurturing, pipeline management, analytics, and interview scheduling. Lever emphasizes relationship-driven hiring, proactive sourcing workflows, and building long-term talent communities beyond immediate open roles.

The Mokka Difference:

Lever excels at managing candidate relationships and recruiting workflows over time. Mokka integrates with Lever to add evidence-based screening depth for active applicants—verifying qualifications and generating rich profiles before candidates enter relationship-building stages.

• Mokka Complements Lever: We work well with Lever customers who want to add screening intelligence to their relationship management platform. Lever manages candidate relationships, nurturing, and pipeline progression. Mokka determines which applicants are worth adding to that pipeline through evidence-based pre-screening and integrity verification.
• Relationship Management vs. Evidence-Based Screening: Lever's strength is building and nurturing candidate relationships over time through CRM-style workflows. Mokka's strength is screening active applicants through structured pre-screening interviews—generating verifiable evidence of accomplishments before candidates enter relationship-building stages.
• Talent Community Building vs. Applicant Verification: Lever helps you build long-term talent communities and maintain relationships with passive candidates. Mokka helps you screen the active applicants who apply right now—cross-checking their claims against resumes, LinkedIn profiles, and third-party data to verify authenticity.
• Pipeline Management vs. Pre-Screening Intelligence: Lever manages how candidates progress through your pipeline stages and nurturing campaigns. Mokka generates the screening intelligence that determines who enters that pipeline—conducting pre-screening interviews that collect accomplishment evidence recruiters can review.
• System of Record vs. Screening Depth: Lever is your system of record for candidate relationships, communications, and recruiting workflows. Mokka provides screening depth that populates Lever with rich, evidence-based candidate profiles—giving recruiters verified data to personalize outreach and relationship-building.
• CRM-Style Automation vs. Interview-Based Evaluation: Lever automates relationship nurturing, follow-ups, and sourcing workflows. Mokka automates pre-screening interviews—collecting specific examples, probing for details, and running integrity checks that relationship management platforms cannot provide.

Key Questions to Consider:

• Before adding candidates to your Lever talent community or nurturing campaigns, how do you verify they're genuinely qualified beyond resume review?
• What screening happens for active applicants before they enter relationship-building workflows—manual review, or automated evidence-based evaluation?
• How do you cross-check candidate claims against LinkedIn profiles, detect AI-generated applications, or verify resume accuracy at scale?
• When building long-term candidate relationships, wouldn't you want those relationships based on verified qualifications rather than polished resumes?
• Can you integrate a screening platform like Mokka with Lever to add evidence-based pre-screening while keeping Lever's relationship management strengths?

Working with Workable

A Quick Look at Workable:

Workable is an all-in-one recruiting platform offering ATS functionality, AI-powered features, sourcing tools, and hiring automation. The platform includes intelligent resume parsing, AI-assisted screening with semantic analysis, one-way video interviews, and passive candidate matching from a 400M+ candidate database. Workable emphasizes end-to-end recruiting solutions for small to mid-sized businesses with easy setup and broad feature coverage.

The Mokka Difference:

Workable is a comprehensive recruiting platform with AI screening features. Mokka integrates with Workable to provide the specialized screening depth that all-in-one platforms—even with AI features—cannot match on their own.

• Mokka Complements Workable: We integrate seamlessly with Workable to enhance its screening capabilities. Workable manages your end-to-end recruiting workflow and provides resume parsing. Mokka adds the evidence-based screening layer—conducting pre-screening interviews, collecting accomplishment proof, and running integrity checks that go beyond resume analysis.
• Resume Parsing vs. Evidence Generation: Workable's intelligent parsing extracts information from resumes with semantic understanding. Mokka generates new evidence through structured pre-screening interviews—collecting specific accomplishment examples, measurable outcomes, and verifiable claims that don't exist on any resume.
• AI-Assisted Screening vs. Integrity-Verified Screening: Workable's AI screening uses semantic analysis to match resumes against job requirements. Mokka cross-checks every pre-screening response against resumes, LinkedIn profiles, and third-party data through our Profile and Answer Integrity analytics—detecting AI-generated applications and inconsistencies.
• One-Way Video vs. Multi-Modal Flexibility: Workable offers one-way video interviews as a screening option. Mokka gives candidates choice of text, voice, or video with no time limits—supporting broader accessibility for candidates uncomfortable on camera, those with disabilities, or anyone who communicates better in writing.
• 400M Candidate Database vs. Your Applicant Pipeline: Workable offers access to 400M+ candidates for passive sourcing when you need more applicants. Mokka screens the candidates who actively apply to your roles—the people who specifically chose your company and are already in your pipeline.
• All-in-One Platform vs. Best-of-Breed Screening: Workable provides broad feature coverage across sourcing, workflow, and basic screening. Mokka specializes exclusively in evidence-based screening depth—purpose-built for pre-screening interviews, accomplishment verification, and integrity analytics that all-in-one platforms cannot match.

Key Questions to Consider:

• When AI parses and screens resumes with semantic analysis, how do you verify candidates actually accomplished what their resumes claim beyond keyword matching?
• How do you detect when candidates use AI to optimize their resumes to match your job descriptions perfectly?
• For one-way video interviews, what mechanisms verify responses aren't rehearsed, scripted, or AI-generated talking points?
• What happens to screening depth if you have accessibility requirements or candidates who communicate better through text than spontaneous video?
• Can you integrate a dedicated screening platform like Mokka with Workable to get specialized pre-screening depth while keeping Workable's all-in-one workflow management?

Part III: Mokka vs. The Competition

Here's how Mokka compares to other screening and recruiting tools you may be evaluating. These are solutions that focus on candidate evaluation, sourcing, or interview automation—distinct from ATS platforms that manage your recruiting workflow.

Mokka vs. HiredScore

A Quick Look at HiredScore:

HiredScore is an AI tool that sits on top of an ATS. Its main job is to read all the resumes and profiles you already have and score them to help recruiters prioritize their worklist.

The Mokka Difference:

HiredScore is excellent at making your recruiters faster at reviewing resumes. Mokka makes them smarter by giving them reliable data that's not on the resume. While HiredScore analyzes the same, often over-optimized resumes, Mokka creates a brand new, richer dataset.

• Creates New Evidence, Not Just Reads Old Resumes: Mokka engages candidates in a pre-screening interview to gather verifiable proof of their accomplishments. We don't just sort the pile faster; we improve the quality of the pile from the start.
• Proactively Verifies Integrity: With AI-generated resumes becoming common, our built-in Profile and Answer Integrity checks actively look for red flags and inconsistencies, ensuring the information you're basing decisions on is authentic.
• Avoids Vendor Lock-In: HiredScore is owned by Workday, and its full potential shines for Workday users. Mokka is ATS-agnostic, giving you the flexibility to change your core HR systems in the future without losing your advanced screening capabilities.

Key Questions to Consider:

• How can you be sure a candidate's score is based on real achievements, not just well-placed keywords on a resume they optimized to match the job description?
• What specific tools are in place to detect if a candidate used AI to write their entire resume, or if there are major inconsistencies between their resume and their LinkedIn profile?
• What happens to your process efficiency if you decide to migrate away from your current ATS in the future?

Mokka vs. Covey

A Quick Look at Covey:

Covey is an AI-powered sourcing tool. It scours platforms like LinkedIn to find potential candidates based on their resume and profile, and then automates email outreach to fill the top of your hiring funnel.

The Mokka Difference:

Covey is a powerful search engine for talent, but it's still searching through resumes and public profiles that can be easily tailored to look good. After Covey finds you 100 potential candidates, how do you decide who to actually interview? Mokka provides the critical next step.

• Provides Deep Qualification, Not Just Broad Sourcing: Mokka provides an in-depth, evidence-based screening that separates the genuinely great candidates from those who just have a polished profile.
• Automates Tedious Pre-Screening: We automate the initial data collection part of the pre-screen, validating the claims made on a resume and gathering actual proof of a candidate's accomplishments. This wins back time for your team to focus on interviewing the best-fit candidates.
• Offers a Rich Candidate Experience: Mokka's interactive, multi-modal screening makes candidates feel seen and respected. We help you build your employer brand with every single applicant, ensuring a positive experience that reflects well on your company.

Key Questions to Consider:

• After a candidate is identified based on their resume or profile, what is the process for verifying the accomplishments they list?
• How does the screening process differentiate between a candidate's self-reported skills and their proven ability to apply those skills to drive measurable results?
• What integrity checks are in place to assess a candidate's honesty or detect if their application answers were generated by an AI assistant?

Mokka vs. Interviewer.ai

A Quick Look at Interviewer.ai:

Interviewer.ai is a tool for one-way, pre-recorded video interviews. It uses AI to analyze a candidate's resume, their answers, and even their facial expressions and tone of voice to generate a score.

The Mokka Difference:

Interviewer.ai can tell you if a candidate is a polished presenter. Mokka tells you if they were a top performer. We believe hiring decisions should be grounded in verifiable facts, not on an AI's interpretation of someone's tone of voice—a "big brother" approach that can feel invasive to candidates and introduce bias.

• Focuses on Verifiable Facts, Not Subjective Impressions: Mokka deliberately focuses on what a candidate has accomplished, not how they present it. Our candidate-friendly process offers the choice of text, voice, or video, allowing everyone to communicate comfortably and equitably.
• Offers a Better, More Compliant Experience: This focus on objective, verifiable evidence is not only fairer and more compliant, but it also leads to a better experience that top candidates appreciate.
• Provides Predictable Pricing: Their per-interview pricing model creates unpredictable costs, while Mokka's seat-based plan gives you full control over your budget and encourages you to screen everyone.

Key Questions to Consider:

• How does an analysis of facial expressions or voice tone avoid penalizing candidates who are neurodivergent, non-native speakers, or simply uncomfortable on camera, but are otherwise top performers?
• What verifiable, fact-based evidence—completely independent of a video recording—is being collected to validate a candidate's past accomplishments?
• Does the pricing model encourage you to screen all your candidates, or does it create unpredictable costs that force your team to manually pre-screen applicants first?

Mokka vs. Jack and Jill AI

A Quick Look at Jack and Jill AI:

Jack and Jill AI is a two-sided AI recruiting marketplace. "Jack" serves as an AI career coach for candidates, scanning 10,000+ jobs per hour and applying on their behalf with no resume or cover letter required. "Jill" works with employers to understand hiring needs and reaches thousands of candidates simultaneously. The business model is contingency-based: companies pay 10% of first-year salary only when a hire is made (half the typical agency fee), with no upfront cost.

The Mokka Difference:

Jack and Jill AI is a contingency recruiting service with an AI front-end. Mokka is a screening technology platform that empowers your internal recruiting team. These are fundamentally different business models.

• Technology Platform vs. Recruiting Agency: Mokka provides software that your team uses to screen applicants. Jack and Jill AI is a recruiting agency that uses AI to source and match candidates, taking a percentage of salary when you hire.
• Your Applicants vs. Their Marketplace: Mokka screens the candidates who apply directly to your company—people who chose you specifically. Jack and Jill AI matches you with candidates from their marketplace who may be talking to multiple companies simultaneously.
• Your Brand, Your Recruiters: With Mokka, your team controls the candidate experience and represents your employer brand. With Jack and Jill AI, "Jack" (their AI agent) represents your company to candidates—you have zero control over how it communicates, what it promises, or how it positions your culture and opportunities.
• Candidate Choice and Accessibility: Mokka gives every candidate the choice to respond via text, voice, or video—meeting diverse needs and preferences. Jack and Jill AI's conversational agents dictate the interaction format.
• Build Internal Capability vs. Outsource to Agency: Mokka strengthens your TA team with screening automation and data ownership. Jack and Jill AI replaces your recruiting function with their AI agents and takes a cut of every hire.
• All Roles vs. Marketplace Availability: Mokka screens any knowledge worker role receiving high application volume. Jack and Jill AI can only match you with candidates actively in their marketplace.
• Predictable SaaS Pricing vs. Contingency Fees: Mokka charges seat-based software fees (predictable, budgetable). Jack and Jill AI charges 10% of first-year salary per hire (unpredictable, scales with headcount and comp levels).

Key Questions to Consider:

• Do you want to build internal recruiting capability, or outsource the function to an agency (even an AI-powered one)?
• What happens to candidates who apply directly to your careers page but aren't in Jack and Jill's marketplace?
• How comfortable are you with an AI recruiter you don't control speaking on behalf of your company to candidates?
• If you hire 50 people per year at $100k average salary, would you rather pay $500k in contingency fees or predictable SaaS pricing?
• Who owns the candidate relationships and data—your team or the agency's AI?

Mokka vs. Micro1

A Quick Look at Micro1:

Micro1 is not a screening tool for your team, but a marketplace of pre-vetted technical talent. Their AI agent, "Zara," finds and interviews engineers, and then presents them to companies for hire, often handling payroll and compliance.

The Mokka Difference:

Micro1 is a strong option when you need to hire a contractor quickly. Mokka is the strategic solution for building your own world-class, long-term team. It empowers your recruiters, strengthens your employer brand with every interaction, and gives you a repeatable process for finding top talent for any role.

• Empowers Your Team vs. Outsourcing Your Hiring: Mokka is a strategic tool designed to make your own recruiting team more effective. Micro1 is a tactical service that replaces that function.
• Builds Your Own Talent Pipeline vs. Offering a Shared Pool: When you use Mokka, you are evaluating 100% of your unique applicant pool. With a marketplace, you are competing with every other client for the same talent.
• Provides a Custom Screen for Your Culture vs. a Generic Vetting Process: Mokka screens candidates against your specific, nuanced requirements for both the role and your unique company culture, ensuring a much higher quality of fit.

Key Questions to Consider:

• How do you ensure that the top candidates in a shared marketplace haven't already been presented to your direct competitors?
• What happens to the rich candidate data and the relationships you build if you decide to stop using a marketplace service? Do you lose access to that talent intelligence?
• How does a generic vetting process account for the unique cultural and team-specific fit requirements that are critical to success and retention at your company?

Mokka vs. Apriora

A Quick Look at Apriora:

Apriora offers an autonomous AI interviewer ("Alex") that conducts real-time, two-way interviews over video or voice, generates structured write-ups after each call, and syncs notes back to the ATS. They position Alex as a domain-expert screener across roles and industries.

The Mokka Difference:

• Evidence over performance: Apriora's core is a live AI interview; Mokka centers on building a multi-source, evidence-based profile (pre-screen interview + integrity checks + profile enrichment), designed to validate accomplishments—not just capture a single interview performance.
• Candidate-friendly optionality: Apriora focuses on live voice/video. Mokka lets candidates choose text, voice, or video with no time pressure—supporting broader accessibility and higher completion.
• Risk & CX posture: The broader category has seen glitches with AI interviewers, underscoring why Mokka emphasizes transparent, reviewable evidence artifacts over "AI-only" conversations.

Key Questions to Consider:

• How are claims from a single live interview verified against other signals (resume, LinkedIn, work samples)?
• What fallbacks exist if an AI call degrades (network, ASR issues)? Are candidates offered alternative modes?
• How are interviewer prompts standardized to reduce variance and support compliance reviews later?

Mokka vs. ConverzAI

A Quick Look at ConverzAI:

ConverzAI provides Voice-AI "Virtual Recruiters" aimed heavily at staffing firms. Their agents run 6–21 minute phone/email/text screenings, summarize results to the ATS, and market "setup in <5 days," with claims of faster placements and large-scale outreach.

The Mokka Difference:

• Staffing vs. in-house depth: ConverzAI is optimized for staffing throughput and omnichannel outreach; Mokka is optimized for evidence quality and integrity analytics for in-house teams and agencies needing trustworthy screening inputs (not just speed).
• Structured recruiter intake: Mokka begins with an explicit recruiter-verified rubric before any candidate engagement, making downstream scoring auditable and role-specific.
• Pricing & lock-in: ConverzAI markets usage-style deployments "with no lock-ins." Mokka's predictable seat pricing encourages screening 100% of applicants without surprise volumes.

Key Questions to Consider:

• For your roles, is the priority omnichannel outreach scale—or verifiable achievements and integrity analysis?
• How are candidate summaries traced back to question-level evidence your managers can review?
• What governance do you get (versioned rubrics, bias checks, audit trails) when regulators ask "how was this decision made?"

Mokka vs. Dex

A Quick Look at Dex:

Dex is an AI-powered recruitment matchmaker focused specifically on software engineers and tech talent. Founded by former Atomico VC executives and backed by a16z, Dex acts as a two-sided marketplace: "Jack" (the AI career agent) works with candidates via conversational voice/chat to understand their goals and apply on their behalf, while the company-facing side matches hiring teams with a small number of highly aligned, motivated candidates.

The Mokka Difference:

Dex is building a curated talent marketplace for tech roles. Mokka empowers your internal recruiting team to screen your entire applicant pipeline—including the unique candidates who only apply to you.

• Your Pipeline vs. Shared Marketplace: Mokka evaluates 100% of your inbound applicants with custom screening criteria. With Dex, you're competing with other companies for the same pre-vetted talent pool, and you lose access to candidates who only applied directly to your company.
• Build Your Team vs. Outsource Recruiting: Mokka strengthens your internal TA function with tools and data ownership. Dex is a marketplace service where the AI agent owns the candidate relationship, not your recruiters.
• Your Brand, Your Voice: With Mokka, candidates interact with a screening process you control and that represents your employer brand. With Dex, an AI agent you don't control represents your company to candidates—you have no say in how it communicates, what promises it makes, or how it positions your culture.
• Candidate Choice and Comfort: Mokka gives candidates the choice to respond via text, voice, or video—meeting them where they're comfortable. Dex's "Jack" AI agent conducts conversational interviews on its terms, not the candidate's preference.
• All Roles vs. Tech-Only: Dex focuses exclusively on software engineering roles. Mokka screens knowledge workers across all functions—marketing, operations, finance, product, customer success, and yes, engineering too.
• Custom Fit vs. Generic Matching: Mokka screens against your specific role requirements and company culture. Dex provides candidates it deems "highly aligned" based on its own matching algorithm.

Key Questions to Consider:

• What happens to candidates who apply directly to your careers page but aren't in Dex's marketplace? How do you screen them?
• If Dex presents a candidate to you and your competitor simultaneously, who wins? How do you differentiate?
• How comfortable are you with an AI agent you don't control representing your employer brand to candidates?
• When you stop using a marketplace service, do you lose access to the candidate data and relationships you've built?

Mokka vs. Findem

A Quick Look at Findem:

Findem is a talent intelligence and sourcing platform that uses "3D data" (people, company, and time dimensions) built from 1.6 trillion data points. It's designed to help talent acquisition teams discover, engage, and nurture candidates through attribute-based search (not just keywords), multichannel outreach, pipeline analytics, and diversity insights. Findem targets mid-market to enterprise customers with primarily enterprise-level contracts (minimum 3-month engagements).

The Mokka Difference:

Findem is a powerful sourcing engine that finds candidates across the internet. Mokka is a screening engine that evaluates the candidates who actually apply to your roles. These are complementary problems, not competing solutions.

• Sourcing vs. Screening: Findem helps you find candidates when you're struggling with low applicant volume or hard-to-source roles. Mokka helps you screen candidates when you're drowning in 100-200+ applications per role. Different pain points.
• Outbound vs. Inbound: Findem excels at proactive outreach and talent community nurturing. Mokka excels at automating the top-of-funnel screening for inbound applicants, freeing up your recruiters to focus on outreach.
• Data Breadth vs. Evidence Depth: Findem's strength is broad talent data aggregation across the market. Mokka's strength is deep, verifiable evidence collection through structured pre-screening interviews with your actual applicants.
• Enterprise Sourcing Platform vs. Screening Automation: Findem is an enterprise platform requiring multi-month commitments and custom configuration. Mokka works off-the-shelf in 5 minutes with seat-based pricing.

Key Questions to Consider:

• After Findem helps you source 100 potential candidates, how do you decide which 10 to actually interview? What's your screening process?
• For roles receiving high inbound application volume, how does a sourcing platform help reduce the manual screening bottleneck?
• Can your team benefit from both tools—Findem for sourcing hard-to-fill roles, and Mokka for screening high-volume inbound applications?

Mokka vs. HeyMilo

A Quick Look at HeyMilo:

HeyMilo offers conversational, AI-led pre-screens and interviews via phone/web audio/video, with ATS integrations (e.g., Lever), auto-engagement via SMS/Email/WhatsApp, transcripts/summaries, and configurable question sets & rubrics.

The Mokka Difference:

• From conversation to evidence: Like HeyMilo, Mokka runs structured, adaptive pre-screens—but we pair this with Profile & Answer Integrity checks and third-party enrichment to produce a trust layer recruiters can rely on.
• Deep intake before automation: HeyMilo emphasizes fast, adaptive interviews; Mokka anchors on recruiter-approved requirements first (critical/must-have/nice-to-have, caps, weighting), which then drive consistent scoring.
• Workflow posture: HeyMilo's materials highlight ATS integrations and usage-based options. Mokka keeps your ATS as the system of record while providing seat-based, unlimited-app volume for predictable budgets.

Key Questions to Consider:

• Can hiring managers open a candidate's Mokka/HeyMilo profile and trace every score to concrete, reviewable evidence?
• How are integrity risks (AI-authored answers, off-screen help, profile mismatches) detected and flagged?
• Does the pricing model encourage you to screen everyone, or only a subset?

Mokka vs. HireVue

A Quick Look at HireVue:

HireVue is an enterprise platform for video interviewing, assessments, and hiring automation; it acquired Modern Hire in 2023, consolidating a large assessment + virtual job tryout portfolio under one roof. Historically, HireVue discontinued facial-expression analysis in 2021 after scrutiny, while maintaining science-based assessments and interview analytics.

The Mokka Difference:

• No face/voice inference: Mokka does not assess facial expressions or paralinguistics; we focus on verifiable achievements collected through structured pre-screens and external signals—designed to be auditable and candidate-friendly.
• Fast setup vs. enterprise programs: HireVue excels at enterprise I/O-backed assessments and large configurations; Mokka aims for 5-minute off-the-shelf activation with strong ATS flow-through, ideal when you need immediate screening coverage without heavy program design.
• Budget predictability: Mokka's seat-based model avoids per-interview volatility common in traditional video-interview tooling.

Key Questions to Consider:

• Do you need full-blown assessments—or faster, fairer pre-screens that generate auditable evidence and plug into your ATS?
• How will your team review/defend decisions (records, scoring logic, explainability) if audited under laws like NYC Local Law 144 or EU AI Act?
• What's the change-management cost to stand up enterprise assessments vs. a lighter, recruiter-led screen?

Mokka vs. Tezi AI

A Quick Look at Tezi:

Tezi positions an agentic, Slack-native AI recruiter ("Max") that autonomously sources, screens, and schedules, and claims a built-in dataset of ~750M profiles. It emphasizes 24/7 availability, proactive follow-ups, and instant processing of large applicant volumes.

The Mokka Difference:

• Agent in Slack vs. evidence in ATS: Tezi's Slack-first agent helps teams delegate tasks conversationally. Mokka focuses on generating role-aligned, integrity-checked evidence and posting rich profiles directly into your ATS for hiring-manager consumption.
• Control & governance: Mokka's recruiter-verified intake, objective rubrics, and answer/profile integrity analytics create a consistent, defensible process—especially important when agentic systems auto-advance steps.
• Cost/ownership posture: Tezi markets replacing multiple tools with its agent and data bundle; Mokka is ATS-agnostic and focused on owning your process & evidence rather than a proprietary sourcing pool.

Key Questions to Consider:

• If an agent sources and screens, how do you audit why candidates were advanced or rejected?
• Where does the candidate data live long-term (ATS vs. vendor system), and what happens if you switch?
• Can hiring managers inspect the underlying evidence—beyond summary scores—before interviews?

Mokka vs. Maki People

A Quick Look at Maki:

Maki provides autonomous AI HR agents that conduct the entire hiring journey—from screening and scheduling to AI-led interviews via phone, SMS, web, or video. Founded in 2022 and backed by $28.6M Series A (January 2025), Maki targets Fortune 500 enterprises (H&M, BNP Paribas, PwC, Deloitte, FIFA, Capgemini) with claims of 80% automation, 3x faster time-to-hire, and evaluation of 300+ skills using proprietary fine-tuned LLMs grounded in psychometric science.

The Mokka Difference:

• Evidence-Based Pre-Screening vs. Fully Autonomous AI Interviews: Maki's AI agents conduct interviews autonomously via phone and video. Mokka provides structured pre-screening that generates verifiable evidence for human review—maintaining human judgment in hiring decisions while automating data collection.
• Candidate Choice vs. AI-Directed Interviews: Maki's AI agents control the interview format and flow. Mokka gives candidates choice of text, voice, or video with no time pressure, supporting broader accessibility, inclusiveness and comfort levels—leading to higher completion rates and better candidate experience.
• Transparent Process vs. Proprietary Black Box: Maki uses proprietary fine-tuned LLMs with undisclosed training data and decision logic. Mokka provides transparent, auditable scoring with clear evidence trails that hiring managers can review and defend.
• Predictable Seat-Based Pricing vs. Enterprise Contracts: Maki targets Fortune 500 companies with enterprise-level contracts and custom pricing. Mokka offers predictable seat-based or consumption-based pricing that scales with your team, not your hiring volume—accessible for both enterprise and mid-market companies.
• Full Automation vs. Human-in-the-Loop: Maki emphasizes 80% automation of screening and interviewing. Mokka balances automation with human oversight—recruiters review AI-generated insights and make final decisions, maintaining accountability and compliance.
• Integrity Verification Layer: Mokka's Profile and Answer Integrity checks actively flag inconsistencies between resumes, LinkedIn profiles, and interview responses. This trust layer is critical when AI-generated applications are increasingly common.

Key Questions to Consider:

• How comfortable are your candidates with an AI agent conducting their entire interview via phone, without human interaction until later stages?
• When an autonomous AI agent rejects a candidate, can you explain the decision with concrete evidence to support legal compliance and candidate feedback requests?
• What happens if a candidate has accessibility needs, language barriers, or technical issues during an AI-led phone interview? What fallback options exist?
• For mid-market companies with 50-200 employees, is an enterprise-focused platform with Fortune 500 pricing accessible and practical?
• How do you maintain your employer brand and candidate experience when an AI agent—not your team—represents your company throughout the screening process?

Mokka vs. First

A Quick Look at First:

First is an AI-powered hiring platform that embeds screening directly into the application form. Instead of adding a separate screening step, First's "smart, adaptive questions capture behavioral data beyond CVs" within the application itself. They offer a freemium model (unlimited users/roles/applications, pay only for AI assessments) with a free tier of 50 monthly assessments, integrations with major ATS platforms (Ashby, Workable, Greenhouse, Bullhorn), and a free ATS option for teams without existing systems. First claims 4.6/5 candidate satisfaction from 6,136+ candidates and emphasizes speed—auto-advancing the top 1-5% of applicants the same day.

The Mokka Difference:

First screens candidates during the application process. Mokka screens candidates after they apply—enabling deeper evidence collection, multi-source verification, and integrity checks that aren't possible at the application stage.

• No Integrity Verification Layer: First analyzes application responses in isolation. Mokka's Profile and Answer Integrity analytics cross-check every candidate's pre-screening responses against their resume, LinkedIn profile, and third-party data sources—automatically flagging inconsistencies, suspicious patterns, and potential AI-generated applications. This trust layer is critical when candidates can use AI to generate perfect answers to screening questions.
• Application Questions vs. Evidence-Based Pre-Screening: First adds behavioral questions to the application form. Mokka conducts structured pre-screening interviews after application, probing for specific, verifiable accomplishments and building a rich, multi-source evidence profile that goes far beyond what candidates self-report on an application.
• Recruiter-Verified Requirements vs. Automated Screening: First's screening happens automatically based on application responses. Mokka begins with a comprehensive recruiter intake process where your team reviews and signs off on detailed requirements (critical/must-have/nice-to-have, caps, weighting) before any candidate is assessed—ensuring screening aligns with your team's real priorities, not just automated pattern matching.
• Speed vs. Trustworthy Evidence: First emphasizes auto-advancing the top 1-5% the same day for speed. Mokka prioritizes trustworthy, verifiable evidence—ensuring the candidates you advance are backed by integrity-checked data, not just fast responses that could be AI-generated or exaggerated.
• Single-Source Data vs. Multi-Source Enrichment: First only analyzes what candidates provide in the application. Mokka enriches every profile with third-party data, LinkedIn verification, resume cross-checks, and structured interview evidence—creating a 360-degree view that reveals what a resume alone cannot.
• Application-Stage Screening vs. Post-Application Depth: Screening at the application stage means making decisions with incomplete information before you have their full resume and context. Mokka's post-application approach means you already have complete applicant data before investing in deep AI screening—allowing smarter, more informed decisions about who to assess.
• Usage-Based Costs vs. Predictable Pricing: First's freemium model charges per AI assessment, creating unpredictable costs as application volume grows and discouraging you from screening everyone. Mokka's seat-based pricing includes unlimited applications, encouraging you to screen 100% of your pipeline without budget anxiety.
• Candidate Experience Tradeoffs: First claims 4.6/5 satisfaction, but adding screening to the application risks abandoning top candidates who have multiple offers and limited patience for lengthy forms. Mokka's 4.7/5 rating comes from candidates who've already chosen to apply and appreciate a respectful, flexible screening process (text/voice/video options, no time limits, privacy controls) that helps them showcase their accomplishments.
• Free ATS vs. Deep ATS Integration: First offers a free ATS option, creating vendor lock-in where your candidate data lives in their system. Mokka integrates deeply with your existing ATS (keeping it as your system of record), is ATS-agnostic, and preserves your flexibility to switch systems without losing screening capability or data ownership.

Key Questions to Consider:

• When a candidate provides perfect answers to screening questions embedded in an application, how do you verify those answers aren't AI-generated or exaggerated?
• What mechanisms detect inconsistencies between what a candidate says in the application form versus what's on their resume, LinkedIn profile, or other public records?
• How does adding AI screening to the application form impact completion rates for top candidates who have multiple offers and limited patience for lengthy applications?
• If your application volume doubles unexpectedly during a hiring surge, how does usage-based AI assessment pricing impact your recruiting budget?
• What happens to your candidate data, screening workflows, and historical analytics if you decide to migrate away from their free ATS to an enterprise system?
• How do you trace screening decisions back to specific, reviewable evidence when a candidate requests feedback or challenges a rejection?

Mokka vs. HiPeople

A Quick Look at HiPeople:

HiPeople is an AI hiring platform that automates screening, video interviews, skills assessments, and reference checks. They claim to qualify candidates "in under an hour" with 24/7 availability across 113 languages. The platform offers free access and serves 2,000+ teams with a 4.6/5 G2 rating, emphasizing speed—moving candidates through continuous evaluation flows combining multiple assessment types.

The Mokka Difference:

HiPeople optimizes for speed through automated video interviews and rapid assessments. Mokka optimizes for trust through multi-source verification and integrity analytics that ensure the candidates you advance are backed by verifiable evidence, not just fast responses.

• No Integrity Verification Layer: HiPeople screens resumes and conducts video interviews without cross-checking responses against LinkedIn profiles, third-party data, or detecting AI-generated applications. Mokka's Profile and Answer Integrity analytics automatically flag inconsistencies and suspicious patterns across multiple data sources.
• Video-Only Interviews vs. Candidate Choice: HiPeople emphasizes 24/7 video interviewing across 113 languages. Mokka gives candidates the choice of text, voice, or video with no time pressure—supporting broader accessibility for neurodivergent candidates, those with language barriers, or anyone uncomfortable on camera.
• Single-Source Screening vs. Multi-Source Enrichment: HiPeople analyzes what candidates provide (resume + video interview responses). Mokka enriches every profile with LinkedIn verification, third-party data, resume cross-checks, and structured interview evidence—creating a 360-degree view that reveals what a resume and video alone cannot.
• Speed vs. Evidence Depth: HiPeople claims to qualify candidates "in under an hour" through rapid screening. Mokka prioritizes building trustworthy, verifiable evidence through post-application pre-screening—ensuring the candidates you advance aren't just fast responders but demonstrably qualified performers.
• Recruiter-Verified Requirements vs. Automated Assessments: HiPeople's automated assessments and fraud detection run without explicit recruiter intake. Mokka begins with comprehensive recruiter review of detailed requirements (critical/must-have/nice-to-have, caps, weighting) before any candidate is assessed—ensuring screening aligns with your team's real priorities.
• Post-Application Depth vs. Continuous Flow: HiPeople screens immediately after application through continuous evaluation flows. Mokka's post-application approach means you already have complete applicant data before investing in deep AI screening—allowing smarter decisions about who to assess with full context.
• Freemium Model Questions vs. Predictable Pricing: HiPeople advertises "free access" but doesn't disclose when or how costs scale as volume grows. Mokka's seat-based pricing includes unlimited applications, giving you budget predictability and encouraging you to screen 100% of your pipeline without surprise charges.
• Assessment Platform vs. Evidence Engine: HiPeople is a comprehensive assessment platform with skills tests, reference checks, and interviews. Mokka is laser-focused on top-of-funnel pre-screening that generates rich, integrity-checked evidence profiles—integrating deeply with your ATS rather than replacing multiple HR functions.

Key Questions to Consider:

• When candidates record video interview responses, how do you verify those answers aren't rehearsed, scripted, or coached by someone off-screen?
• What mechanisms detect if a candidate used AI to generate perfect answers to screening questions before recording their video response?
• How does requiring video interviews impact completion rates for candidates with accessibility needs, language barriers, or those simply uncomfortable on camera?
• What does "free access" mean at scale? When do costs kick in, and how are they calculated as application volume grows?
• How do you trace screening decisions back to specific, reviewable evidence when a candidate requests detailed feedback on why they were rejected?
• Can hiring managers cross-check interview responses against LinkedIn profiles and public records to verify consistency before advancing candidates?

Mokka vs. Endorsed

A Quick Look at Endorsed:

Endorsed is an AI recruiting platform that screens applications, detects resume fraud, and sources candidates from 750+ million professional profiles across 28+ data sources. They claim to evaluate "1,000 profiles every 20 seconds" with bias-audited evaluations and transparent reasoning. Pricing starts at $299/month for 3 jobs (Pro plan), scaling to custom enterprise pricing with unlimited jobs and SSO.

The Mokka Difference:

Endorsed focuses on rapid resume screening at massive scale with fraud detection. Mokka focuses on evidence-based pre-screening through structured interviews that generate new, verifiable data rather than just analyzing existing resumes faster.

• Resume Analysis vs. Evidence Generation: Endorsed screens resumes and detects fabricated claims using their "FraudShield" across existing documents. Mokka conducts structured pre-screening interviews that generate new evidence of accomplishments—going beyond what's written on a resume to collect verifiable, specific examples you can review.
• Speed Claims vs. Depth of Verification: Endorsed advertises "1,000 profiles every 20 seconds" processing speed. Mokka prioritizes thorough multi-source verification (resume + LinkedIn + pre-screening interview + third-party data)—ensuring candidates aren't just quickly sorted, but deeply validated before advancing.
• Fraud Detection Focus vs. Integrity Analytics: Endorsed's FraudShield detects fabricated resume claims. Mokka's Profile and Answer Integrity analytics go further—cross-checking pre-screening responses against resumes, LinkedIn profiles, and detecting AI-generated interview answers, not just resume fraud.
• Skills-First Screening vs. Evidence-Based Accomplishments: Endorsed emphasizes "skills-first evaluation" from resume data. Mokka probes for specific, measurable accomplishments with concrete evidence—differentiating between candidates who list skills and those who can demonstrate impact through verifiable achievements.
• Per-Job Pricing vs. Unlimited Applications: Endorsed's $299/month Pro plan includes 3 jobs, with custom pricing for more. Mokka's seat-based pricing includes unlimited job requisitions and unlimited applications—encouraging you to screen every role and every candidate without budget anxiety.
• Sourcing + Screening vs. Screening Depth: Endorsed offers AI sourcing across 28+ data sources alongside screening. Mokka is laser-focused on screening your existing applicant pipeline with maximum depth and integrity verification—integrating with your ATS as the primary system of record.
• Bias Auditing vs. Recruiter-Controlled Requirements: Endorsed emphasizes post-hoc bias auditing of AI decisions. Mokka puts recruiters in control upfront—comprehensive intake with explicit requirement prioritization (critical/must-have/nice-to-have) ensures screening criteria reflect your team's values before any AI evaluation begins.
• Chrome Extension Convenience vs. ATS-Native Integration: Endorsed offers a Chrome extension for in-ATS fraud detection. Mokka integrates bi-directionally with your ATS at a deeper level—posting rich candidate profiles directly into your system and syncing all decisions seamlessly.

Key Questions to Consider:

• After AI screens 1,000 resumes in 20 seconds, how do you verify the top candidates actually accomplished what they claim beyond detecting obvious fabrications?
• What happens when a candidate has a legitimate but unusual career path that doesn't fit typical resume patterns—does rapid AI screening flag them as fraudulent?
• How does per-job pricing impact your willingness to screen all roles versus cherry-picking high-priority positions?
• When "bias-audited" AI makes a decision, can hiring managers see the specific evidence that drove the recommendation, or just aggregate scores?
• How does AI sourcing from 750M profiles differ from candidates who specifically chose to apply to your company? Are marketplace candidates more or less engaged?
• What's the candidate experience when AI screens their resume in seconds—do they feel evaluated or just sorted?

Mokka vs. Sense

A Quick Look at Sense:

Sense is a talent engagement platform using conversational AI to automate candidate communication across SMS, WhatsApp, email, and chat. Their "Grace AI Recruiter" handles sourcing, matching, engagement, and scheduling with deep ATS integrations (Taleo, Workday, Greenhouse, SAP, Bullhorn). Sense targets enterprise clients (Dell, Coca-Cola, Kaiser Permanente, HCA Healthcare) and claims 75% reduction in time-to-offer. Pricing is not disclosed.

The Mokka Difference:

Sense is a multi-channel engagement and communication platform that automates recruiter outreach and scheduling. Mokka is an evidence-based screening platform that automates candidate evaluation and qualification. These solve different problems in the hiring funnel.

• Engagement vs. Evaluation: Sense excels at candidate communication—automated messaging, follow-ups, scheduling, and keeping candidates warm. Mokka excels at candidate evaluation—structured pre-screening interviews, integrity verification, and generating evidence-based qualification profiles. Different pain points.
• Outbound Communication vs. Inbound Screening: Sense is built for recruiters managing high-touch, multi-channel outreach campaigns. Mokka is built for teams drowning in 100-200+ applications per role who need automated, trustworthy screening to identify top candidates.
• Scheduling Automation vs. Pre-Screening Automation: Sense's AI handles interview scheduling and calendar management. Mokka's AI handles the pre-screening interview itself—collecting accomplishment evidence, probing for specifics, and cross-checking responses for integrity.
• Communication Platform vs. Screening Engine: Sense provides the infrastructure for recruiter-candidate conversations at scale. Mokka provides the evaluation layer that determines which candidates deserve those conversations—generating rich profiles that recruiters can review before outreach.
• Enterprise ATS Focus vs. ATS-Agnostic Depth: Sense emphasizes deep integrations with enterprise ATS platforms (Taleo, Workday, SAP). Mokka works with any ATS as an independent screening layer, preserving flexibility to switch systems without losing screening capability.
• Multi-Channel Messaging vs. Multi-Modal Interviewing: Sense offers SMS, WhatsApp, email, and chat for recruiter-initiated communication. Mokka offers text, voice, and video options for candidate-initiated responses to screening questions—supporting accessibility and comfort.
• Database Cleanup vs. Profile Enrichment: Sense includes deduplication and database cleanup features. Mokka enriches candidate profiles with new data (LinkedIn verification, third-party sources, structured interview evidence) rather than just organizing existing records.
• Complementary Tools, Not Competitors: Many teams could benefit from both—Sense for managing candidate communication and engagement at scale, Mokka for screening and qualifying candidates before recruiters invest time in personalized outreach.

Key Questions to Consider:

• After Sense helps you engage 1,000 candidates via multi-channel campaigns, how do you decide which 50 to actually screen and evaluate?
• For roles receiving high inbound application volume, how does a communication platform help reduce the manual screening bottleneck?
• What evidence-based screening happens before your recruiters invest time in SMS campaigns and personalized outreach?
• Can your team benefit from both tools—Sense for engagement and scheduling, Mokka for screening and qualification?
• How does Sense's "Grace AI Recruiter" evaluate candidate quality beyond matching keywords in resumes?
• When an AI agent handles candidate communication, how do you maintain your employer brand voice and ensure compliance with messaging content?

Mokka vs. Tofu

A Quick Look at Tofu:

Tofu is an AI resume screening platform that ranks candidates based on "real experience, skills, and attributes" while detecting fraud across 4+ billion data points. They integrate with 32+ ATS platforms and offer a talent marketplace of pre-vetted candidates who passed assessments. Tofu uses a proprietary "Fraudbase" built from 5M+ analyzed profiles to identify fake applications. Pricing is not disclosed; demos required.

The Mokka Difference:

Tofu analyzes resumes to rank candidates and detect fraud. Mokka goes beyond resume analysis by conducting structured pre-screening interviews that generate new, verifiable evidence of accomplishments you can't find in any resume.

• Resume Ranking vs. Evidence Generation: Tofu's AI ranks candidates based on resume content and fraud signals. Mokka conducts structured pre-screening interviews that probe for specific, measurable accomplishments—generating new evidence beyond what's written on any resume.
• Fraud Detection vs. Integrity Verification: Tofu's "Fraudbase" detects fake resumes using patterns from 5M+ profiles. Mokka's Profile and Answer Integrity analytics cross-check pre-screening responses against resumes, LinkedIn profiles, and third-party data in real-time—detecting not just fake resumes but also AI-generated interview answers and inconsistent claims.
• Custom Agents vs. Recruiter-Verified Requirements: Tofu trains custom agents on "previous successful hires or sourced profiles." Mokka starts with comprehensive recruiter intake where your team explicitly reviews and approves detailed requirements (critical/must-have/nice-to-have, caps, weighting)—ensuring screening reflects current priorities, not just historical patterns.
• Resume Analysis at Scale vs. Candidate-Friendly Interviews: Tofu processes resumes rapidly to surface top candidates. Mokka provides candidates with flexible, respectful pre-screening (text/voice/video choice, no time limits, privacy controls) that helps them showcase accomplishments—achieving 4.7/5 satisfaction and 40-90% completion rates.
• Talent Marketplace vs. Your Applicant Pipeline: Tofu offers access to a marketplace of "interview finalists" who passed assessments. Mokka screens the unique candidates who apply directly to your company—people who specifically chose you, not a shared talent pool where you compete with other employers.
• ATS Integrations vs. ATS-Agnostic Depth: Tofu integrates with 32+ ATS platforms for data flow. Mokka integrates deeply with your ATS while remaining independent—posting rich candidate profiles and syncing decisions without creating vendor lock-in if you switch systems.
• Bias Removal Focus vs. Evidence-Based Evaluation: Tofu emphasizes removing bias through resume analysis. Mokka focuses on collecting verifiable evidence of accomplishments (with examples, metrics, outcomes) that hiring managers can review and question—making evaluation auditable and defensible.
• Profile Enrichment vs. Profile Verification: Tofu enriches applicant profiles with data from 4B+ data points. Mokka verifies profile claims through multi-source cross-checking (resume vs LinkedIn vs pre-screening interview vs third-party data)—ensuring enriched data is accurate, not just abundant.

Key Questions to Consider:

• When AI ranks candidates based on resume analysis, how do you verify the top-ranked candidates actually accomplished what they claim beyond detecting obvious fraud?
• What happens when a high-potential candidate has an unconventional resume format or career path that doesn't match your "previous successful hires" pattern?
• How does relying on historical hiring patterns account for evolving role requirements or deliberate efforts to diversify your team?
• What's the candidate experience when an AI screens their resume in isolation—do they get a chance to explain context, provide examples, or clarify unusual career decisions?
• For the talent marketplace: Are candidates applying to you specifically, or are they being matched to multiple companies simultaneously? How do you differentiate?
• How do hiring managers trace screening decisions back to specific resume sections or fraud signals when candidates request feedback?

Mokka vs. Symbal

A Quick Look at Symbal:

Symbal is an AI-first, all-in-one recruiting platform using autonomous AI agents for sourcing, candidate engagement, phone screening, evaluation, and scheduling. Built "by former CHROs of Liberty Mutual, NYC, & Flexport," Symbal claims 70% reduction in time-to-fill, 22 hours saved weekly per recruiter, and 95% candidate satisfaction. The platform offers "100x candidate sourcing," 24/7 multilingual outreach, structured phone dialogues, and custom rubric scoring. Pricing is not disclosed; targets enterprise organizations.

The Mokka Difference:

Symbal is an all-in-one autonomous recruiting platform that replaces multiple recruiting functions with AI agents. Mokka is a specialized screening platform that integrates with your existing recruiting stack to provide evidence-based candidate evaluation while keeping humans in control.

• Autonomous AI Agents vs. Human-in-the-Loop: Symbal emphasizes fully autonomous AI agents handling sourcing through scheduling without human intervention. Mokka provides AI-powered pre-screening that generates evidence for human review—recruiters make final advancement decisions based on transparent, auditable data.
• All-in-One Platform vs. Best-of-Breed Integration: Symbal replaces your ATS or works independently "from spreadsheets." Mokka integrates deeply with your existing ATS (Greenhouse, Ashby, Workable, etc.) as the screening layer—keeping your ATS as the system of record and preserving your recruiting tech stack.
• Phone-Only Screening vs. Multi-Modal Accessibility: Symbal conducts structured phone dialogues for candidate evaluation. Mokka offers candidates choice of text, voice, or video with no time limits—supporting broader accessibility for candidates uncomfortable with phone calls, those with hearing impairments, or non-native speakers who prefer written responses.
• 100x Sourcing Claims vs. Applicant Pipeline Focus: Symbal emphasizes massive sourcing scale ("100x candidate sourcing"). Mokka focuses on screening the candidates who actually apply to your roles—the people who specifically chose your company and are already in your pipeline.
• Custom Rubric Scoring vs. Integrity-Verified Evidence: Symbal uses custom rubrics to score phone screening responses. Mokka goes beyond scoring—our Profile and Answer Integrity analytics cross-check every response against resumes, LinkedIn profiles, and third-party data to verify claims before generating scores.
• Enterprise-Only Positioning vs. Scalable Deployment: Symbal targets enterprise organizations with former CHRO expertise and 22-hour-per-week savings claims. Mokka offers predictable seat-based pricing accessible to mid-market companies (50-200 employees) and enterprises alike, with 5-minute setup.
• Autonomous Scheduling vs. Evidence-Based Qualification: Symbal's AI handles interview scheduling automatically. Mokka's AI handles the qualification interview itself—collecting specific accomplishment examples, probing for details, and building rich evidence profiles that determine who deserves to be scheduled.
• Time-to-Fill Focus vs. Quality-of-Hire Focus: Symbal emphasizes 70% reduction in time-to-fill through automation. Mokka prioritizes quality of advancement—ensuring the candidates you interview are backed by verifiable, integrity-checked evidence, not just rapid AI decisions.

Key Questions to Consider:

• When autonomous AI agents make sourcing, screening, and advancement decisions without human oversight, how do you audit why candidates were selected or rejected for compliance reviews?
• What happens if a candidate has accessibility needs or technical issues during an AI-led phone screening—are alternative formats offered, or are they disqualified?
• How comfortable are you with an AI platform that works "independently from spreadsheets" rather than integrating with your ATS as the system of record?
• When "custom rubric scoring" evaluates phone responses, can hiring managers see the actual candidate answers and context, or only aggregate scores?
• How does "100x candidate sourcing" from AI agents differ from candidates who proactively applied to your company? Which group is more engaged and culture-fit?
• For mid-market companies (50-200 employees), is an enterprise-focused platform built by Fortune 500 CHROs accessible and practical for your team size and budget?
• What happens to your candidate data, recruiting processes, and team capabilities if you decide to move away from an all-in-one platform back to best-of-breed tools?

Mokka vs. Lily

A Quick Look at Lily:

Lily is an AI-powered phone screening platform that conducts automated conversations with candidates, positioning itself as "Human-Centred AI." The platform captures interview insights through structured phone questions while emphasizing the preservation of human judgment in final hiring decisions. Pricing and detailed product information are not publicly disclosed.

The Mokka Difference:

Lily focuses exclusively on phone-based screening conversations. Mokka offers candidates choice of text, voice, or video with comprehensive multi-source verification—supporting broader accessibility while building deeper evidence profiles.

• Phone-Only vs. Multi-Modal Accessibility: Lily conducts automated phone screenings with structured questions. Mokka gives candidates the choice of text, voice, or video responses with no time pressure—supporting candidates uncomfortable with phone calls, those with hearing impairments, speech difficulties, or non-native speakers who prefer written communication.
• Conversation Capture vs. Evidence Verification: Lily captures interview insights from phone conversations. Mokka goes further—cross-checking every response against resumes, LinkedIn profiles, and third-party data through our Profile and Answer Integrity analytics to verify claims and detect AI-generated answers.
• Automated Questions vs. Recruiter-Verified Requirements: Lily's structured phone dialogues follow predefined question sets. Mokka begins with comprehensive recruiter intake where your team explicitly reviews and approves detailed requirements (critical/must-have/nice-to-have, caps, weighting) before any candidate is assessed—ensuring screening reflects your team's actual priorities.
• Phone Conversation Logs vs. Rich Evidence Profiles: Lily provides interview insights from phone calls. Mokka creates comprehensive evidence profiles combining pre-screening interview responses, accomplishment examples with metrics, LinkedIn verification, resume cross-checks, and integrity flags—giving hiring managers reviewable evidence beyond conversation summaries.
• Single-Channel Screening vs. Multi-Source Enrichment: Lily analyzes what candidates say during phone calls. Mokka enriches every profile with multiple data sources (resume + LinkedIn + pre-screening interview + third-party verification)—creating a 360-degree view that reveals what a single phone conversation cannot.
• Human-Centered Positioning vs. Candidate-Friendly Experience: Lily emphasizes preserving human decision-making in hiring. Mokka achieves this while also optimizing the candidate experience—4.7/5 satisfaction rating with 40-90% completion rates because candidates can choose their preferred communication method and work without time pressure.

Key Questions to Consider:

• When candidates respond to automated phone screening questions, how do you verify those answers aren't rehearsed or influenced by someone coaching them off-screen?
• What happens if a candidate has a hearing impairment, speech difficulty, or is in a loud environment during the scheduled phone screening—are alternative formats offered?
• How are phone conversation insights verified against resumes, LinkedIn profiles, and third-party data to detect inconsistencies or AI-generated prep?
• What's the candidate experience for non-native speakers or those who communicate more effectively in writing than speaking spontaneously?
• Can hiring managers access actual phone conversation transcripts with full context, or only summarized "insights"?
• How does phone-only screening impact completion rates compared to offering candidates their choice of communication method?

Mokka vs. Purplefish

A Quick Look at Purplefish:

Purplefish develops ultra-realistic AI voice agents that conduct phone screening interviews for recruiters. Headquartered in NYC and backed by 8VC, Purplefish claims to screen "10x more candidates in 1/10th the time" with 70%+ reduction in time-to-fill. The platform processes candidates from application to fully-screened in under 10 minutes, including nights and weekends, supporting 5,000+ job types with secure ATS/CRM integrations. Pricing is not publicly disclosed.

The Mokka Difference:

Purplefish optimizes for ultra-fast phone screening throughput with realistic voice AI. Mokka optimizes for trustworthy, multi-source evidence collection that candidates appreciate and hiring managers can verify.

• Voice-Only AI Calls vs. Candidate Choice: Purplefish conducts realistic voice AI phone screenings as the only interaction method. Mokka offers candidates choice of text, voice, or video with no time limits—supporting accessibility for those uncomfortable with AI phone calls, hearing impairments, or communication preferences.
• Speed Focus vs. Evidence Depth: Purplefish emphasizes screening candidates in under 10 minutes from application to fully-screened. Mokka prioritizes building verifiable evidence through thorough pre-screening that cross-checks responses against multiple sources—ensuring candidates aren't just rapidly sorted, but deeply validated.
• Ultra-Realistic Voice vs. Integrity Verification: Purplefish focuses on making voice AI interactions feel human-like to reduce candidate pressure. Mokka focuses on verifying that candidate responses are authentic—our Profile and Answer Integrity analytics cross-check interview answers against resumes, LinkedIn profiles, and third-party data to detect AI-generated prep or inconsistencies.
• 10-Minute Processing vs. Comprehensive Enrichment: Purplefish processes candidates to "fully-screened" status in under 10 minutes. Mokka takes the time needed to enrich profiles with multi-source verification, accomplishment probing, and integrity checks—because "fully screened" should mean "deeply validated," not just "rapidly questioned."
• Voice Agent Training vs. Recruiter-Controlled Criteria: Purplefish configures voice agents on company-specific processes and identity. Mokka puts recruiters in control upfront—comprehensive intake with explicit requirement prioritization (critical/must-have/nice-to-have, caps, weighting) ensures screening reflects your team's current values and priorities.
• 15 Hours Saved on Calls vs. 15 Hours Invested Strategically: Purplefish saves recruiters 15 hours/week from unqualified candidate calls. Mokka saves similar time while generating higher-quality evidence profiles—so recruiters spend those reclaimed hours on candidates backed by verified accomplishments, not just quick phone responses.
• Voice-Specific Candidate Experience vs. Multi-Modal Accessibility: While Purplefish customers note AI calls feel "straightforward" and "reduce pressure," Mokka's 4.7/5 satisfaction and 40-90% completion rates come from candidates who appreciate choosing their communication method—text for written clarity, voice for personal touch, or video when appropriate.

Key Questions to Consider:

• When AI voice agents screen candidates in under 10 minutes, how do you verify responses aren't rehearsed talking points prepared with AI assistance?
• What happens if a candidate has a thick accent, hearing impairment, or is in a noisy environment during the AI voice call—are alternative formats offered?
• How are ultra-realistic voice AI responses cross-checked against LinkedIn profiles and resumes to ensure consistency and authenticity?
• What's the completion rate when candidates are required to complete phone screenings versus being offered their choice of text, voice, or video?
• Can hiring managers review actual conversation transcripts with full context, or only AI-generated summaries of the 10-minute calls?
• For candidates who communicate more effectively in writing than speaking spontaneously, how does phone-only screening impact their ability to showcase accomplishments?

Mokka vs. Popp

A Quick Look at Popp:

Popp is an AI recruitment platform designed primarily for staffing firms, serving as a layer between their ATS and talent team. The platform automates assessment across text, voice, and video submissions with integrations to 30+ ATS/HR systems including Workday, SAP, and Bullhorn. Popp claims to process 6,000 applications per hour (versus 76 manually), achieve 83% reduction in cost-per-hire, and improve candidate NPS from 34 to 73. Customers include PageGroup, Randstad, Robert Walters, and AMN Healthcare. The platform emphasizes GDPR, SOC2 Type II, and EU AI Act compliance.

The Mokka Difference:

Popp is optimized for high-volume staffing firm throughput across multiple clients. Mokka is optimized for in-house recruiting teams building their own talent pipeline with deep evidence verification and employer brand control.

• Staffing Firm Throughput vs. In-House Depth: Popp processes 6,000 applications/hour for staffing agencies managing multiple client companies. Mokka provides evidence-based screening for in-house teams managing their own unique applicant pipeline—focusing on quality of fit for your specific culture and requirements, not rapid sorting for multiple clients.
• Multi-Client Automation vs. Your Employer Brand: Popp automates recruitment tasks across 30+ ATS systems for staffing firms representing many companies. Mokka integrates with your single ATS to maintain your employer brand and candidate relationships—every interaction represents your company specifically, not a staffing agency.
• 3-Second Review vs. Evidence-Based Evaluation: Popp reduces time-to-review from 3 minutes to 3 seconds per application. Mokka focuses on generating verifiable evidence through structured pre-screening interviews—prioritizing trustworthy insights over raw speed, ensuring advancement decisions are defensible.
• Text/Voice/Video Assessment vs. Integrity-Verified Interviews: Popp evaluates submissions across multiple formats. Mokka goes further—our Profile and Answer Integrity analytics cross-check every response against resumes, LinkedIn profiles, and third-party data to verify claims and detect AI-generated content.
• Volume Hiring Focus vs. Quality Hiring Focus: Popp is designed for staffing firms filling hundreds of roles monthly across diverse clients. Mokka is designed for companies building long-term teams where cultural fit, integrity verification, and evidence-based advancement matter more than processing velocity.
• Cost-Per-Hire Reduction vs. Quality-of-Hire Improvement: Popp emphasizes 83% reduction in cost-per-hire for staffing operations. Mokka emphasizes quality of advancement—ensuring candidates are backed by verified evidence before expensive recruiter time is invested.
• Staffing Agency NPS vs. Employer Brand Experience: Popp improved candidate NPS from 34 to 73 for staffing firm interactions. Mokka achieves 4.7/5 satisfaction with candidates applying directly to your company—building your employer brand, not an agency's reputation.

Key Questions to Consider:

• If you're an in-house recruiting team, not a staffing firm, how does a platform optimized for multi-client agency work apply to building your specific talent pipeline?
• When AI processes 6,000 applications per hour, how much evidence-based depth can each candidate receive versus being rapidly sorted by keywords?
• What happens to your employer brand when recruitment automation is designed for staffing agencies representing multiple companies, not your unique culture?
• How are 3-second application reviews verified for integrity (AI-generated responses, resume mismatches, LinkedIn inconsistencies) before candidates advance?
• For roles requiring strong cultural fit and long-term retention, does cost-per-hire reduction matter more than quality-of-hire improvement?
• When staffing firms manage candidate relationships, what control do you have over candidate experience and your employer brand reputation?

Mokka vs. Tenzo

A Quick Look at Tenzo:

Tenzo is an AI recruiting co-pilot platform backed by Microsoft AI, Yahoo, Visa, and Abstract Ventures. The platform automates sourcing (continuous web searches), screening (multi-channel interviews via email, SMS, phone, video), and scheduling (24/7 calendar coordination) across 40+ languages. Tenzo claims to reduce time-to-hire by up to 40% while surfacing "only the top 1% of candidates." The platform serves Fortune 1000 manufacturers and mid-market organizations with ATS integrations including Indeed Apply. Pricing is not publicly disclosed.

The Mokka Difference:

Tenzo is an all-in-one automation platform handling sourcing, screening, and scheduling. Mokka is a specialized screening platform laser-focused on evidence-based candidate evaluation with integrity verification—integrating with your existing recruiting stack.

• All-in-One Automation vs. Best-of-Breed Screening: Tenzo automates sourcing, screening, and scheduling across the hiring funnel. Mokka specializes in deep, evidence-based screening that integrates with your existing ATS and tools—enhancing your recruiting stack without replacing it.
• Continuous Sourcing vs. Applicant Pipeline Focus: Tenzo's AI agents conduct 24/7 web searches to source candidates. Mokka focuses on screening the candidates who actually apply to your roles—the people who specifically chose your company and are already in your pipeline.
• Multi-Channel Interviews vs. Integrity-Verified Responses: Tenzo conducts automated interviews via email, SMS, phone, and video. Mokka goes beyond collecting responses—our Profile and Answer Integrity analytics cross-check every answer against resumes, LinkedIn profiles, and third-party data to verify authenticity and detect AI-generated content.
• Top 1% Claims vs. Verifiable Evidence: Tenzo claims to surface "only the top 1% of candidates" through automated filtering. Mokka provides hiring managers with the actual evidence—interview responses, accomplishment examples, integrity check results, LinkedIn verification—to make their own informed judgments about who's truly in the top tier.
• 40% Faster Hiring vs. Evidence-Based Quality: Tenzo emphasizes 40% reduction in time-to-hire through automation. Mokka prioritizes quality of advancement—ensuring candidates are backed by verified, multi-source evidence before expensive recruiter time is invested, even if it takes slightly longer.
• AI-Driven Scheduling vs. Human-Controlled Advancement: Tenzo's AI handles 24/7 scheduling automation. Mokka's AI generates evidence for human review—recruiters make final advancement and scheduling decisions based on transparent, auditable data, maintaining accountability.
• Multi-Language Support vs. Multi-Modal Accessibility: Tenzo supports 40+ languages for global hiring. Mokka offers candidates choice of text, voice, or video in their preferred language—supporting not just language diversity but also communication preferences and accessibility needs.

Key Questions to Consider:

• When AI agents conduct continuous sourcing and automated screening across multiple channels, how do you audit why certain candidates were selected or filtered out?
• What happens to candidates who apply directly to your careers page but aren't found through Tenzo's web sourcing—do they get the same evaluation depth?
• How are automated interview responses across email, SMS, phone, and video verified for consistency and authenticity before determining the "top 1%"?
• Can hiring managers review the actual evidence behind screening decisions, or only AI-generated summaries and candidate rankings?
• For roles requiring strong cultural fit, how does automated sourcing from the general web compare to candidates who specifically chose to apply to your company?
• When AI handles scheduling for automated candidates, how do you maintain your employer brand voice and candidate relationship quality?

Mokka vs. Hallo

A Quick Look at Hallo:

Hallo is an AI-powered language proficiency assessment platform available across 60+ languages. The platform evaluates speaking, writing, listening, and reading skills simultaneously through five scenario-based, open-response questions, delivering CEFR proficiency scores with detailed feedback on fluency, vocabulary, grammar, pronunciation, and coherence. Hallo maintains SOC II Type 2, ISO 27001, and GDPR certifications with monthly third-party AI audits for bias and fairness. The platform integrates with ATS systems like Greenhouse, Workday, and Lever, targeting pre-employment screening, customer service/BPO operations, and international workforce management.

The Mokka Difference:

Hallo is a specialized language proficiency testing platform. Mokka is a comprehensive candidate screening platform. These are complementary tools, not direct competitors—Hallo tests language skills, Mokka evaluates job qualifications and accomplishments.

• Language Testing vs. Job Qualification Screening: Hallo assesses whether candidates meet language proficiency requirements (CEFR levels) for roles. Mokka evaluates whether candidates meet job requirements—experience, accomplishments, skills, and cultural fit—across any role type, not just language-dependent positions.
• Pronunciation & Grammar Focus vs. Accomplishment Evidence: Hallo analyzes clarity, intonation, vocabulary, and grammar. Mokka probes for specific, measurable accomplishments with concrete evidence—differentiating candidates who can communicate clearly from those who have demonstrably excelled in past roles.
• Single Skill Assessment vs. Comprehensive Evaluation: Hallo tests one dimension (language ability). Mokka conducts comprehensive pre-screening covering past role alignment, accomplishment validation, requirement matching, and integrity verification—building a complete candidate profile.
• Specialized Use Case vs. Universal Application: Hallo is essential for customer service, BPO, international teams, and language-critical roles. Mokka screens candidates for any knowledge worker role receiving high application volume—engineering, marketing, operations, finance, product, customer success, and yes, language-dependent roles too.
• Complementary Not Competitive: Many teams could benefit from both—Hallo for verifying language proficiency requirements, Mokka for evaluating overall job qualifications and accomplishments. If a role requires Spanish fluency + 5 years of sales experience, Hallo validates the Spanish, Mokka validates the sales accomplishments.
• Language-Specific Integrity vs. General Application Integrity: Hallo includes anti-cheating technology for language tests. Mokka's Profile and Answer Integrity analytics cross-check job qualification responses against resumes, LinkedIn profiles, and third-party data—detecting AI-generated answers and inconsistencies across your entire applicant pipeline.

Key Questions to Consider:

• For roles that don't require specific language proficiency (monolingual markets, non-customer-facing positions), how does a language testing platform help screen candidates?
• After verifying a candidate meets language requirements, how do you evaluate their actual job qualifications, accomplishments, and cultural fit?
• Can your team benefit from both tools—Hallo for language-critical role requirements, Mokka for comprehensive job qualification screening?
• How do you screen candidates for roles where language is one of many requirements, not the primary qualification?

Mokka vs. SquarePeg

A Quick Look at SquarePeg:

SquarePeg is an AI-powered recruiting platform emphasizing "glass-box AI" transparency that enriches resumes beyond keyword matching. The platform offers applicant screening, talent rediscovery (identifying previous applicants for new roles), passive sourcing (500M+ profiles), fraud detection, and outcome intelligence. SquarePeg integrates with 50+ ATS systems (Greenhouse, Ashby, Lever, Workable) and targets modern technology companies at growth stage. Customers report reducing screening time from hours to 10 seconds per candidate. Pricing follows a "pay for the job posts you need" model with free trial options.

The Mokka Difference:

SquarePeg focuses on resume enrichment and passive sourcing through data aggregation. Mokka focuses on evidence generation through structured pre-screening interviews with integrity verification—creating new data rather than just analyzing existing resumes better.

• Resume Enrichment vs. Evidence Generation: SquarePeg enriches resumes with company context, skill inference, and external data. Mokka conducts structured pre-screening interviews that generate new evidence of accomplishments—going beyond any resume to collect specific, measurable examples you can review.
• Glass-Box Resume Analysis vs. Integrity-Verified Interviews: SquarePeg provides transparent explanations for resume-based scores. Mokka provides the actual evidence—interview responses, accomplishment examples, integrity check results—so hiring managers can make their own judgments, not just trust AI scoring.
• Past Applicant Rediscovery vs. Current Applicant Depth: SquarePeg identifies previous applicants matching new roles with career updates. Mokka provides deep evaluation of current applicants—cross-checking responses against LinkedIn profiles, resumes, and third-party data to verify claims and detect AI-generated content.
• Passive Sourcing Database vs. Active Applicant Pipeline: SquarePeg offers access to 500M+ professional profiles for passive candidate discovery. Mokka screens the candidates who actively apply to your roles—people who specifically chose your company, often the most engaged and culture-fit candidates.
• 10-Second Screening vs. Comprehensive Verification: SquarePeg reduces screening to 10 seconds per candidate through automated resume scoring. Mokka invests the time needed for multi-source verification—ensuring candidates aren't just quickly scored, but deeply validated through Profile and Answer Integrity analytics.
• Company Data Enrichment vs. Candidate Response Validation: SquarePeg infers skills and enriches profiles with company information from employment history. Mokka validates actual accomplishments through pre-screening interviews—differentiating candidates who worked at impressive companies from those who actually drove measurable impact.
• Outcome Intelligence Predictions vs. Evidence-Based Decisions: SquarePeg uses predictive analytics to forecast hiring timelines and refine requirements. Mokka provides verifiable evidence profiles so hiring managers can make informed decisions based on actual candidate accomplishments, not predictive models.
• Pay-Per-Job-Post vs. Unlimited Applications: SquarePeg charges per job post. Mokka's seat-based pricing includes unlimited job requisitions and unlimited applications—encouraging you to screen every role and every candidate without per-post budget constraints.

Key Questions to Consider:

• When AI enriches and scores resumes in 10 seconds, how do you verify the top-scoring candidates actually accomplished what their enriched profiles suggest?
• What happens when a candidate has an unconventional resume that doesn't match typical company/skill patterns—does rapid enrichment help or hurt their chances?
• For passive candidates sourced from a 500M+ database, how do you assess their genuine interest in your company versus active applicants who chose to apply?
• How does "glass-box" transparency showing AI resume scoring logic compare to reviewing actual candidate interview responses and accomplishment evidence?
• When talent rediscovery surfaces previous applicants with career updates, how do you verify those updates are accurate versus reviewing fresh pre-screening interview evidence?
• Can hiring managers trace screening decisions back to specific, reviewable candidate responses, or only to AI-inferred skills from resume analysis?

Mokka vs. Classet

A Quick Look at Classet:

Classet offers an AI voice recruiter named "Joy" that conducts phone interviews with candidates. The platform generates transcripts and summaries from these voice conversations but, according to Warden's description, does not score candidates based on the interview content. Classet uses usage-based pricing ($599-$1,999/month) and targets high-volume hiring sectors like retail, hospitality, and hourly workforce recruitment.

The Mokka Difference:

Classet focuses on phone interview transcription for high-volume hourly hiring. Mokka provides comprehensive evidence-based screening with scoring and integrity verification for knowledge worker roles.

• Transcription vs. Evaluation: Classet's "Joy" conducts phone interviews and generates transcripts without scoring. Mokka conducts pre-screening interviews and provides structured evaluation—scoring candidates against your specific requirements with clear evidence trails that hiring managers can review.
• Phone-Only vs. Multi-Modal Accessibility: Classet requires candidates to complete voice phone interviews. Mokka gives candidates choice of text, voice, or video with no time limits—supporting accessibility for those with hearing impairments, speech difficulties, thick accents, or anyone who communicates more effectively in writing.
• Hourly Workforce Focus vs. Knowledge Worker Screening: Classet targets high-volume hourly hiring in retail and hospitality. Mokka is designed for knowledge worker roles—engineering, marketing, operations, product, finance—where accomplishment evidence and cultural fit matter as much as availability and basic qualifications.
• Summary Generation vs. Integrity Verification: Classet provides interview summaries from phone conversations. Mokka cross-checks every response against resumes, LinkedIn profiles, and third-party data through our Profile and Answer Integrity analytics—detecting AI-generated prep, inconsistencies, and verifying claims.
• Usage-Based Pricing vs. Predictable Seats: Classet charges monthly usage fees ($599-$1,999/month). Mokka offers predictable seat-based pricing with unlimited applications—encouraging you to screen 100% of your pipeline without budget anxiety as volume fluctuates.

Key Questions to Consider:

• If the AI phone interviewer generates transcripts without scoring, how do you efficiently evaluate hundreds of candidates from those unstructured conversation logs?
• What happens when a candidate has a hearing impairment, thick accent, or is in a noisy environment during the phone screening—are alternative formats offered?
• For knowledge worker roles requiring accomplishment evidence and cultural fit assessment, does phone transcription provide sufficient evaluation depth?
• How are phone interview responses verified against resumes and LinkedIn profiles to detect inconsistencies or AI-generated prep?

Mokka vs. Juicebox

A Quick Look at Juicebox:

Juicebox offers "PeopleGPT," an AI-powered platform for LinkedIn sourcing and candidate ranking with autopilot functionality. The platform automates the search for potential candidates across LinkedIn and other professional networks, ranking them based on fit and enabling automated outreach. Juicebox focuses on the sourcing side of recruiting—finding and engaging passive candidates at scale.

The Mokka Difference:

Juicebox is a sourcing tool that finds candidates who aren't actively applying. Mokka is a screening tool that evaluates candidates who are applying. These solve opposite problems in the hiring funnel.

• Sourcing vs. Screening: Juicebox excels when you don't have enough applicants—it finds passive candidates on LinkedIn and ranks them by potential fit. Mokka excels when you have too many applicants—it screens your active applicant pipeline with evidence-based evaluation. Different pain points.
• Passive Candidate Ranking vs. Active Applicant Verification: Juicebox ranks passive candidates based on LinkedIn profiles and public data. Mokka screens active applicants through structured pre-screening interviews—collecting new evidence of accomplishments and verifying claims against multiple sources.
• Outbound Automation vs. Inbound Screening: Juicebox automates outreach to passive candidates you're pursuing. Mokka automates screening of inbound candidates who've already applied—the people who specifically chose your company.
• Profile-Based Potential vs. Evidence-Based Performance: Juicebox identifies candidates with impressive-looking LinkedIn profiles. Mokka validates whether candidates actually accomplished what they claim—through pre-screening interviews, integrity checks, and multi-source verification.
• Complementary Tools, Not Competitors: Many teams use both—Juicebox for sourcing hard-to-find talent when applicant flow is low, Mokka for screening high-volume applications when your careers page is flooded. They address opposite ends of the recruiting challenge.

Key Questions to Consider:

• After Juicebox sources 100 passive candidates with strong LinkedIn profiles, how do you screen and evaluate them to decide who's worth interviewing?
• For roles receiving 150+ inbound applications, how does a sourcing tool help reduce your manual screening bottleneck?
• What's the completion rate when you invite passive candidates (found via sourcing) to complete screening versus active applicants who already chose to apply?
• Can your team benefit from both—Juicebox for sourcing when applicant volume is low, Mokka for screening when volume is high?