Data enrichment is the process of appending verified external facts, filings, code history, hiring records, prior outcomes, to a raw name or company, so a thin data point becomes a complete profile you can actually score. Without it, a scoring system has nothing to grade but a title and a LinkedIn URL.
A raw record starts as almost nothing: a name, an email, a company domain. Enrichment pulls in everything that already exists in public and licensed sources and attaches it to that record, incorporation filings, code activity, press mentions, prior company outcomes, patent filings, hiring history, ATS notes.
Enrichment on its own does not produce judgment. It produces the raw signal that an evidence-based scoring engine needs before it can score anything at all.
Enrichment used to be a research associate's afternoon: pulling company records, checking code repositories, reading old press pieces one browser tab at a time. That work is now automated at the top of the funnel. A 2026 survey of nearly 300 private capital dealmakers found 85% now use AI to automate daily tasks, up from 76% a year earlier, and 82% specifically use AI for deal sourcing research, which is enrichment work in practice. For a fund or a portfolio company running evidence-based scoring, that automation only matters if the underlying facts are accurate. A model can weigh enriched data perfectly and still produce a bad score if the enrichment itself is stale or wrong.
On the hiring side, the same discipline applies to every open role a portfolio company is filling. A candidate scored well needs enrichment on their actual commits, their actual prior company's trajectory, and their actual reference outcomes, not just what's printed on a resume.
Enrichment adds information. Verification confirms that information is true and current. The two get conflated often, but a scoring system needs both: enrichment as the input layer, and a confidence bar that a fact has to clear before it's allowed to move a score. ScoringFactory treats enrichment strictly as the first step. See how that shows up end to end in how we tie every score to a line of evidence.
No. Scraping collects raw data from a source. Enrichment goes a step further: it matches that data to an existing record, resolves conflicts across sources, and attaches it to the right person or company so it's usable.
It depends on what's being scored. A founder's cap table history barely moves month to month; a candidate's most recent shipped work or a company's latest funding round can go stale within weeks. ScoringFactory timestamps every enriched fact so an old input never silently outweighs a fresh one.
Yes, if the sources themselves are uneven, for example, richer public records for candidates from well-covered companies. The fix isn't less enrichment, it's evidence-based scoring: every score traces to the specific fact that earned it, so a thin record shows up as a gap to investigate rather than a silent penalty.
Bring a founder you're diligencing. We'll score them against your bar, live, with the receipts behind every number.