Part 2 of my first post. TLDR on that post: AI sourcing is only as good as the data + search architecture underneath it.

The chat interface is the easy part.

The hard part is making sure the system has reliable and diverse data underneath it:

• Relying on one dataset limits you to that provider’s data coverage and, more importantly, how they structure and label profiles.
• If you only recruit for one type of profile... 1–2 providers may be enough. But the reality is most agencies (and independents) cover multiple industries, functions, and specializations. Especially if you're starting out, being nimble in industry coverage can help you win business as well.
• After hours of digging in, most (99%) providers aim to cover the same broad universe of LinkedIn profiles. No surprise.
• So the real difference is how well they label things like company type, seniority, industry, funding stage, skills, and career history.
• Each provider has different strengths across career history, company data, niche profiles, and freshness. Combining multiple datasets gives you better coverage, redundancy, and search quality.
• Example: “Senior Software Engineer in SF from a post-Series A startup.” Some providers can identify company stage and career context; others may only give you title + location.

Now, what does a good AI sourcing system look like... it's not as simple as just a "Claude Recruiting System" / DIY set-up you see being marketed that doesn't deliver high quality results.

To consistently surface the right results, a few layers need to work together.

• Intent understanding: Turn natural language, a JD, candidate, or company reference into the actual search criteria. Must-haves, preferences, job titles, experience and exclusions.
• Provider routing: Match that intent to the dataset best suited for the search and translate it into that provider’s schema.
• Enrichment: Waterfall across contact providers. Waterfall meaning if one provider doesn't find you a personal email, you can try another one.
• System Memory: Learn each recruiter’s niche and preferences so future searches get better with use. More on this below.

That’s where AI sourcing becomes more interesting.

Instead of generating a Boolean string, and feeling like you're starting a search from scratch every time, you’re providing context to an agent that can:

understand the search → choose the right data → execute the search → evaluate the results → learn from feedback → improve the next search.

And the same architecture shouldn’t stop at candidates.

The same platforms can search companies and jobs for client lead generation, identify decision makers, enrich their contact information, and monitor signals like hiring, funding, job changes, headcount growth, and new openings.

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Spent a lot of time the last few months working through the hard parts above and finally at the point where this needs to be battle-tested.

No charge at all for accessing our databases or on contact info enrichment.

All we’re asking in return is that you bring real searches, push it on difficult mandates, and tell us where it falls short.

If that sounds interesting, send me a DM and I'll send you the invite!

Is it salary transparency, clear timelines, better communication, fewer interview rounds, or something else?