The difference between a pile of records and a connected picture is one hard problem.
2026-08-20 · 6 min read
Entity resolution is the process of deciding when separate records, often from different sources and in different formats, refer to the same real-world entity, such as one company. It is the quiet problem underneath every company profile, KYB check, and due-diligence report, and it is much harder than it looks.
The naive approach is to match records by name. It fails in both directions. Two different companies can share a name (there are many Acme LLCs across states), so matching on name wrongly merges them. And one company is written many ways, abbreviated, misspelled, with and without Inc, as a doing-business-as name, so matching on name misses records that belong together.
In business and property data, the wrong-merge error is the dangerous one: it attaches one company's liens or lawsuits to another. A resolver that optimizes for catching every possible match ends up corrupting the data it was supposed to clean.
Reliable entity resolution leans on the identifiers that survive across sources: registration numbers, the federal UEI, EIN, and parcel keys for property. These are unique and stable in a way names are not. When two records share a strong identifier, they are the same entity; when they only share a name, that is a weak hint, not proof.
This is why the join is more valuable than the raw data. Anyone can acquire public records; connecting them correctly on strong identifiers is the hard, defensible part.
Because a wrong merge is worse than a missed match, good entity resolution is biased toward precision. Records join only when the evidence genuinely connects them; weaker links are kept separate rather than guessed. The result is a graph you can trust: one entity per real company, with all of its records attached, and no phantom connections.
For an AI agent, entity resolution is the difference between a useful answer and a confident wrong one. Ask an agent about a company and, without resolution, it sees scattered near-duplicates. With resolution, it sees one entity and its full footprint, and can reason over ground truth instead of guessing which records belong together.
Related but broader. Deduplication collapses duplicate rows within one dataset; entity resolution links records across many different sources to one real-world entity.
Names are neither unique nor stable. Distinct companies share names, and one company is written many ways. Merging on name alone creates false entities, so strong identifiers must carry the join.
Entity resolution API · What is entity resolution · Due diligence
Keep reading: How to run public-record due diligence on a company · Permit-based lead generation: finding businesses about to spend · KYB vs KYC: what business verification actually checks · What public records reveal about a commercial property · How to give an AI agent access to public records (MCP)