~700 developer profiles. One week. For Turing.com.

The client

Turing.com is a unicorn talent marketplace that helps companies build engineering teams remotely. Sourcing at their volume means evaluating developers by verified code — not resumes — which is exactly what GitMatcher is built for.

The problem

High-volume developer sourcing hits the same wall everywhere: LinkedIn profiles say what candidates claim; GitHub shows what they actually ship. Screening hundreds of profiles by hand doesn't scale, and keyword search floods the pipeline with false positives — people with a language in their bio who haven't shipped meaningful code in it.

What we did

1. Mined the GitHub code graph. Identified developers through real contribution history in the target stack — not self-reported skills.
2. Scored every profile. Each candidate was scored on code quality signals, architectural depth, and contribution velocity against the role's bar.
3. Verified and deduplicated. Active profiles only, with contact paths attached, in a clean export that dropped straight into the client's workflow.

The result

Approximately 700 vetted, scored developer profiles delivered in one week — a pipeline the client's team could work immediately, with every candidate backed by verifiable public code history.

What this engagement looked like

This was the Scale & Embedded tier: a multi-role, high-volume sprint with delivery tuned to the client's workflow. The same engine runs our fixed-price sprints — one role, one week, $1,500.