Concept
Why AI analysis needs documents and databases
Documents-and-databases analysis combines structured records with unstructured business material so one question can be evaluated across both kinds of source.
Structured data is useful for measurable facts such as revenue, usage, transaction history, dates, and counts. Documents often contain business context that is not represented in a database schema, including contract terms, explanations, decisions, exceptions, and plans.
The two source types are complementary. A database may show what changed, while a document helps explain why. Combining them does not automatically resolve ambiguity: the workflow still needs consistent identifiers, compatible time ranges, appropriate permissions, and a review of which source is authoritative.
In brief
Facts that stand on their own
- Structured data is useful for measurable facts such as revenue, usage, and transaction history.
- Documents often contain business context that is not represented in database schemas.
- A shared account, product, supplier, or project identifier is often needed to connect the two source types.
- Source dates matter because a current database record may conflict with an older document.
- Access rules still apply when an analysis spans more than one source.
Why it matters
Review begins after the answer arrives
Many operational questions have a quantitative half and a contextual half. Treating either half as the complete answer can hide an important exception or produce an explanation that is not supported by the measured result.
This is not primarily a connector problem. The analytical work is deciding which facts belong together, which definitions apply, and how uncertainty or conflict should be represented.
How it works
A practical review sequence
- 01
Identify the measurable part
Use structured records for quantities, dates, status, usage, transactions, and other consistently typed fields.
- 02
Identify the contextual part
Use contracts, reports, notes, plans, and other documents for terms, decisions, exceptions, and explanations.
- 03
Join by business identity
Relate sources through a stable account, supplier, project, product, or other business identifier.
- 04
Align scope and time
Check that the records and documents refer to the same entity, period, definition, and decision context.
- 05
Keep each source inspectable
Preserve the query or records behind measured facts and the passage behind document-based context.
Concrete example
Which customers are at risk of renewal, and why?
A database can identify accounts with an upcoming renewal, quantify annual recurring revenue, summarize product usage, and count support activity. Those fields establish who is in scope and what measurable signals changed.
Contracts and account documents can add renewal terms, QBR notes, customer reports, and account plans. They may explain an exception or concern that is not encoded as a database field. A complete investigation connects the sources by account and period, then keeps the supporting record and passage available for review.
Database facts
- Annual recurring revenue
- Product usage
- Renewal date
- Support tickets
Document context
- Contract terms
- QBR notes
- Customer reports
- Account plans
Failure modes and limitations
What inspectability does not solve
- Names and identifiers may not match across systems.
- Documents may be stale, duplicated, scanned poorly, or missing important context.
- Database fields may use a different business definition from the document author.
- A source may be connected but unavailable to a particular Agent or user because of access rules.
- Conflicting facts should be surfaced for review rather than blended into false certainty.
DataFact's approach
Keep useful support close to the work
DataFact provides separate product paths for document intelligence and database analysis, then lets configured Agents use eligible resources in a workflow. Documents can retain locators used by citations; Datasets define a usable SQL boundary and can carry data-specific knowledge.
The availability of a source, query, citation, chart, or generated file depends on the Agent and the work performed. Reviewers should inspect the artifacts that are present and treat missing support as a limitation.
Questions
Frequently asked questions
Why not put every document into a database first?
Some document structure can be extracted, but terms, explanations, exceptions, and narrative context do not always fit a stable schema. Keeping the original source available preserves context for review.
Can structured and unstructured sources disagree?
Yes. Differences in date, scope, definition, and source authority can create a real conflict that requires review.
How are records connected to documents?
The analysis usually needs a shared business identity such as an account, supplier, product, project, or contract, plus compatible scope and time boundaries.
Does DataFact move database rows into a new warehouse?
No. DataFact connects to existing databases. Datasets define usable boundaries for analysis; they do not replace the source warehouse.
Related concepts