Case Study
MatchCraft Talent Matching SaaS
An AI-assisted talent matching product concept with PRD-driven execution and agent-friendly docs for fast iteration.
SaaSProductLLMNext.jsPostgreSQL
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Problem
Talent sourcing is noisy and manual; matching candidates to roles often depends on inconsistent reviewer judgment and scattered notes.
Constraints
- Needs explainable matching (not just scores)
- Should fit a lean MVP scope for quick validation
- Must support a clean workflow for clients and internal reviewers
Solution
Defined a structured pipeline (intake → normalize → match → shortlist → feedback loop) with a clear PRD/agent workflow so implementation can be delegated to coding agents effectively.
Impact
- Reduced time-to-shortlist with structured intake and ranking
- Improved consistency via repeatable evaluation criteria
- Created a scalable base for iterative matching improvements
