A Quick Look at CARV:
CARV positions itself as an AI platform for recruiters with three modules: Zero-Admin (meeting recording and AI documentation), Zero-Handling (conversational AI for pre-screening), and Zero-Waste (talent pool engagement). They claim 80% fewer admin tasks, 70% reduction in cost per hire, and 3x faster hiring. Enterprise customers include Manpower, Carrefour, DHL, and TMC. CARV holds ISO 42001, ISO 27001, and SOC 2 Type II certifications. In March 2025, CARV acquired Recrubo to expand conversational AI capabilities. ATS integrations include Bullhorn, Job Adder, Workable, and Recruitee.
Understanding CARV's Product Architecture:
CARV originated as a meeting intelligence platform. The core Zero-Admin module joins recruiter-led interviews, records conversations, and uses AI to generate candidate write-ups and populate ATS fields. Candidates in CARV are typically created when they appear on scheduled meetings or phone calls—the system detects external participants and creates profiles from those interactions.
The conversational AI capabilities (Zero-Handling) were added through the Recrubo acquisition in March 2025, offering chatbot-based pre-screening via WhatsApp, text, and voice. Zero-Waste provides talent pool matching and engagement. When evaluating CARV, it's worth clarifying which modules are included and how the recently-acquired features integrate with the core platform.
The Mokka Difference:
CARV excels at automating recruiter documentation and post-interview workflows. Mokka excels at automating pre-interview candidate qualification. These address different bottlenecks in the hiring process.
- Documentation Layer vs. Qualification Layer: CARV's strength is capturing and structuring information from interviews recruiters conduct—generating write-ups, summaries, and ATS updates. Mokka's strength is determining which candidates warrant recruiter time through structured pre-screening that happens before any interview.
- Recruiter-Led Interviews vs. Autonomous Pre-Screening: CARV's documented workflow involves the recruiter conducting interviews while CARV records and documents. Mokka conducts pre-screening interviews autonomously—collecting accomplishment evidence so recruiters only invest time in candidates backed by verified qualifications.
- Meeting-Based Candidates vs. Application-Based Screening: In CARV's core workflow, candidates are created from meeting participants or phone calls. Mokka screens the full volume of inbound applications, qualifying candidates before any meeting is scheduled.
- Post-Interview Insights vs. Pre-Interview Evidence: CARV generates insights after conversations occur. Mokka generates evidence before recruiter conversations—probing for specific accomplishments, asking follow-up questions, and building verification profiles upfront.
- No Integrity Verification Layer: CARV captures what candidates say in conversations. Mokka's Profile and Answer Integrity analytics cross-check every response against resumes, LinkedIn profiles, and third-party data—detecting inconsistencies and AI-generated content that conversation recording cannot identify.
- Staffing Agency Roots vs. In-House Team Focus: CARV's customer base (Manpower, staffing firms) and ATS integrations (Bullhorn, Job Adder) reflect strong staffing agency positioning. Mokka is built for in-house recruiting teams managing their own unique applicant pipeline with deep evidence-based screening.
- Conversational AI Approach: CARV's chatbot-based screening (via Recrubo) uses text/voice agents on WhatsApp and other channels. Mokka gives candidates choice of text, voice, or video with no time limits—supporting broader accessibility and higher completion rates.
- Complementary Use Cases: For teams conducting many recruiter-led phone screens, CARV's meeting documentation can reduce admin burden. Mokka is ideal when the bottleneck is screening high application volumes before those phone screens happen.
Key Questions to Consider:
- What's your primary bottleneck—documenting interviews you already conduct, or screening applications to determine who deserves an interview?
- When CARV's chatbots screen candidates, how are their claims verified against resumes, LinkedIn profiles, and other sources?
- For in-house teams (vs. staffing agencies), how does CARV's workflow fit your recruiting process and ATS?
- What evidence trail exists when candidates are screened via chatbot—can hiring managers review the underlying data, or only AI-generated summaries?
- Which CARV modules are included in your contract, and how integrated are the recently-acquired conversational AI features with the core platform?
- If chatbots handle screening, what mechanisms detect AI-generated or rehearsed candidate responses?