DINQ

DINQ aggregates open-source contributions, academic papers, social profiles, and collaboration graphs to generate capability profiles and trust networks for AI talent, moving beyond keyword matching to deep reasoning. It can automatically surface core contributors to major projects and map trust relationships based on past collaborations, helping organizations pinpoint the real technical leaders.

Compared to traditional headhunters, DINQ dramatically reduces sourcing costs. A preliminary pricing discussion mentioned a monthly fee of $100–$300, far below the typical 20%–30% of annual salary charged by executive search firms. It shortens the sourcing phase, allowing teams to focus on deep engagement and persuasion, making it especially attractive for startups and VCs that need to spot top AI brains without six-figure recruitment bills.

Its main limitations lie in data coverage and domain depth. Assessments heavily depend on candidates’ public footprints on GitHub, Google Scholar, and social networks; individuals with sparse online records or non-public contributions may receive incomplete profiles.