In an AI-assisted research product, the answer is only part of the experience. The user also needs a credible path back to the evidence.

For Kellette, source is not a footnote added after the analysis. It is part of the product architecture.

Opportunity intelligence is a chain of claims

A recommendation may contain several different kinds of statements. Some come directly from the procurement source. Some are structured calculations. Some are comparisons with the company profile. Others are interpretations about fit. Each kind of statement has a different evidence burden.

If those layers are blended into one paragraph, the output may sound confident while becoming difficult to inspect. A user cannot easily tell what the buyer said, what the system calculated, and what the model inferred.

Provenance creates an inspectable path

A strong evidence trail should make it possible to answer questions such as:

• Which source produced this fact?
• When was that source collected?
• Has the source changed?
• Which observed facts influenced the recommendation?
• Which statements are interpretations rather than source claims?
• What material information remains unknown?

This is not about exposing hidden model reasoning. It is about exposing the evidence and the user-facing rationale required to evaluate the decision support.

Why provenance matters more when AI becomes faster

AI can compress a large amount of reading into a short response. That speed is valuable, but it can also remove the friction that once forced a researcher to encounter the source directly.

The solution is not to make AI slower. It is to make the result easier to audit. A concise brief should let the user move from conclusion to rationale to evidence without searching for the source from scratch.

Trust grows when the path from recommendation back to evidence is shorter than the path from evidence to recommendation.

Provenance also improves operations

The same evidence trail that helps a customer verify a recommendation can help the system operate more safely. If a material field fails validation, the workflow can stop. If the source changes, the system can identify which reasoning records may need another look. If a customer challenges a recommendation, the product can preserve the evidence state that produced it.

That turns provenance from a citation feature into infrastructure.

What should never disappear

Kellette’s trust model is being designed around four visible categories:

• Observed: source-grounded facts.
• Derived: structured facts created through deterministic processing.
• Interpretation: Kellette’s labeled analysis of fit or relevance.
• Unknown: material information not established by the available evidence.

Those categories give the user a compact mental model for what they are seeing. They also create a quality standard for the product itself.

The product promise is smaller, and stronger

Kellette should not promise certainty where procurement records do not provide it. It should promise a more disciplined decision-support experience: preserve the source, separate evidence from interpretation, show what changed, keep unknowns visible, and help the company decide whether the opportunity deserves attention.

That is a narrower promise than “AI knows the answer.” It is also a more defensible foundation for an intelligence company.