Spent the last few weekends building an AI sourcing agent. Quick version: feed it a role, it ranks candidates, drafts the outreach. The model layer was the easy part. The data layer is where every project like this quietly dies. Every "B2B data" provider gives you the same five filters — job title, location, company, seniority, industry — and a giant pool of half-stale profiles. So your "AI sourcer" ends up recommending the same 200 people as everyone else's AI sourcer, with last-quarter's job titles. That's not a sourcing agent. That's a list rental. I switched the data layer to DataForB2B and the agent suddenly had something to actually reason over. 70+ filters — including the ones that matter for technical sourcing: GitHub repositories, certifications, languages spoken, years of experience, past employers, funding stage of current company. Profiles are sourced live across 60+ public sources, not pulled from a cached export that's been rotting in a warehouse for nine months.…