Greenhouse: The "Write-Only" Database Problem
The Verdict: Greenhouse is the leader in "structured hiring," but this structure creates a rigid wall against discovery. Users consistently report an inability to search their own candidate database effectively.
The Problem: Keyword vs. Tag
Greenhouse relies heavily on pre-set tags and application questions. If you didn't ask "Do you have 5 years of experience?" as a specific yes/no question on the application form, you cannot easily filter for it later.
The "Mass Search" Failure: Recruiters report they cannot search for keywords inside resumes across their entire database. This means your past applicants—a goldmine of talent—are effectively invisible unless you manually tagged them perfectly at the time of application.
The "Write-Only" Trap
This limitation turns your ATS into a "write-only" database. You pay to store thousands of resumes, but you can't retrieve them when a new role opens. You are forced to pay for new job ads and sourcing tools to find candidates you already have.
How Mokka Fixes It
Mokka turns your Greenhouse database into a searchable talent engine.
Semantic Search: Mokka indexes your candidates and understands context. You can search for "Senior Java Developer with Fintech experience," and Mokka will find them, even if they didn't check a specific box.
Rediscovery: Mokka's AI agent can automatically reach out to past silver-medalist candidates when a new matching role opens, re-engaging them without any recruiter effort.
Keep Greenhouse for the process. Use Mokka for the discovery.