Mokka vs Skima AI

Summary

Mokka and Skima AI both pitch AI-driven search to replace keyword hunting in your ATS or resume pile. The real split: Mokka assumes you're sourcing net-new candidates from external databases and need enrichment to get their emails and phones. Skima AI assumes you already have a resume database-whether in Greenhouse, Lever, or a 10,000-row spreadsheet-and want semantic search across it. Mokka's workflow is outbound: find profiles, enrich contacts, push into sequences. Skima's workflow is inbound: parse what you already own, then ask "show me backend engineers who've scaled Kubernetes in fintech" in plain English and get ranked results without writing a single Boolean operator. Both claim to surface overlooked candidates via ML scoring, but Mokka's scoring runs on fresh data it pulls, while Skima's runs on stale data you fed it months ago. That staleness cuts both ways: Skima won't help if your database is empty or ancient, but it shines when you're drowning in silver-medalist resumes from prior reqs. Mokka's enrichment comes with the usual bounce rate (expect 10-20% bad emails), and both tools require onboarding time-Mokka for query logic, Skima for understanding how its semantic engine interprets your prompts. Pricing is opaque for both, but Skima offers a freemium tier, suggesting it's friendlier to solo recruiters or small teams testing before committing. Mokka's value threshold is higher: you need volume (20+ hires a quarter) to justify the seat cost and the learning curve.