Concept
What is a verifiable AI analyst?
A verifiable AI analyst is an AI system that helps people analyze business information while keeping the supporting information behind important conclusions inspectable.
The work may involve documents, database records, generated queries, calculations, citations, and a sequence of analytical steps. Inspectability gives a reviewer a way to ask where an answer came from, which definitions were applied, and whether the conclusion follows from the available material.
“Verifiable” does not mean AI never makes mistakes. It means a person has more of the information needed to check an answer, identify a weak assumption, reproduce a number, or decide that more evidence is required.
In brief
Facts that stand on their own
- A citation shows where supporting material was retrieved; it does not prove that the material was interpreted correctly.
- A database query can be inspected for tables, fields, joins, filters, and time ranges.
- A document can preserve business context that is absent from a database schema.
- Verification depends on access to the relevant source, not only on the wording of an answer.
- An inspectable workflow can still produce an incomplete or incorrect conclusion.
Why it matters
Review begins after the answer arrives
Business answers are often used outside the conversation that produced them. A finance, operations, or risk team may need to review the source, repeat the calculation, or explain the result to someone who did not ask the original question.
A fluent answer without supporting context makes that review difficult. Keeping useful artifacts visible reduces the amount of guesswork between receiving an answer and deciding whether to act on it.
How it works
A practical review sequence
- 01
Define the question
State the subject, measure, scope, time period, and expected output clearly enough to evaluate the result.
- 02
Inspect the sources
Review the document passages, database records, or other source material that the workflow made available.
- 03
Inspect the method
Check queries, filters, definitions, calculations, and analytical steps when they are present.
- 04
Review the conclusion
Confirm that the answer addresses the question and that its qualifiers match the supporting information.
Concrete example
Investigating a margin change
Suppose an operations leader asks why margin fell on a shipping lane. Database records may establish the change and identify the affected period. A logistics review may explain a temporary surcharge or contract exception.
A reviewer should be able to inspect the query that calculated the margin, the filters that selected the lane and dates, and the document passage used to explain the change. If any of those artifacts is missing, the answer may still be useful, but its verification boundary is narrower.
Failure modes and limitations
What inspectability does not solve
- Supporting material may be unavailable because a source is not connected, indexing is incomplete, or access has changed.
- A visible source can be stale, ambiguous, or non-authoritative.
- A correct query can still answer the wrong business question when the definition or scope is wrong.
- Not every DataFact response contains citations, queries, charts, or files; output depends on the Agent, source type, and workflow.
DataFact's approach
Keep useful support close to the work
DataFact connects supported documents and databases to configured Agents. An answer can include citations, charts, generated files, query context, or execution details when those are produced by the workflow.
DataFact also keeps business knowledge and Analysis Model versions separate from source records. Reviewers should still validate consequential conclusions against authoritative sources and approved business definitions.
Questions
Frequently asked questions
Does verifiable mean guaranteed correct?
No. Inspectable support makes review more practical, but it does not guarantee that the source, query, interpretation, or conclusion is correct.
What should be inspectable?
The useful artifacts depend on the question. They can include source passages, database queries, filters, definitions, calculations, citations, charts, files, or execution steps.
Can an answer be useful without a citation?
Yes, but the reviewer should understand what can and cannot be checked. Some database analyses, tool results, and model-only responses may not contain a document citation.
Is DataFact a claim-level provenance system?
No. DataFact can expose supporting material produced by a workflow, but this page does not claim a complete provenance record for every sentence or claim.
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