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?