AI Resume Matcher

01 / Problem

Early hiring pipelines involve a lot of manual comparison: reading a job description, reading a resume, deciding if the match is worth a call. The process is slow, inconsistent across reviewers, and often misses non-obvious alignment (a candidate with adjacent skills who hasn't used the exact keyword).

02 / Approach

Used structured LLM extraction to pull typed representations of both the JD and resume (skills, experience level, responsibility scope, tone), then scored alignment across those dimensions rather than keyword overlap. The structured extraction step is where most of the quality comes from: once you have clean structured data, the comparison is straightforward.

03 / Trade-offs

Structured extraction via LLM is not free: it adds latency and cost per comparison. For high-volume pipelines you'd want a cached or embedded representation. We also deliberately did not train a classifier, because a fine-tuned model would memorize the biases of whoever labeled the training data. Using a general-purpose model with explicit criteria is more auditable, which matters here.

04 / Status

Shipped and in use. The core matching logic is stable; the UI is minimal by intent.

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