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?