The real problem is evidence assembly
A business rarely loses money because a database did not contain a record. It loses money because the useful records were scattered across portals, spelled inconsistently, stale, or discovered after credit had already been extended. In Indian trade credit, checking a counterparty can mean moving between GST registration details, legal names, court records, directors, addresses, and documents sent over email or WhatsApp. The hard part is not retrieval alone. It is assembling enough evidence to make a decision under time pressure.
That changes how an AI product should be designed. A chat box that produces a confident paragraph is the wrong center of gravity. The product should begin with an entity, show how it resolved that entity, expose the records it found, and separate facts from judgments. Every useful conclusion needs a trail back to a public record or supplied document.
A decision workflow, not a magic score
I think of due diligence as a sequence of gates. First: are we looking at the correct legal entity? Second: is the registration active and are the names and addresses internally consistent? Third: what litigation or compliance signals deserve attention? Fourth: what remains unknown? Only after those gates should a system summarize risk. A single score can be convenient, but convenience becomes dangerous when it hides why the score moved.
The interface should make uncertainty visible. Name matches can be exact, partial, or unresolved. A court result can mention a similar company without referring to the same entity. A missing record is not proof that nothing happened. Good software makes these distinctions easy to scan and difficult to ignore.
Where AI actually earns its place
Language models are useful for normalizing messy names, extracting structured facts from long documents, ranking potentially relevant records, and drafting a concise explanation of the evidence. They are less useful as an oracle. The best architecture keeps deterministic checks and source retrieval outside the model, then asks the model to reason over a bounded evidence packet.
The product test is simple: can a credit manager understand the conclusion, challenge it, and reach the source quickly? If yes, AI has reduced work without removing judgment. If the system only makes the answer sound polished, it has added a new kind of risk. Practical due diligence is not about making uncertainty disappear. It is about making uncertainty legible before money moves.