Knowledge Provenance
Our knowledge layer preserves an evidence path from derived conclusions back to the underlying company records that support them.
Provenance is not confidence
A model can be highly confident and still be wrong. A useful production system needs to know where an asserted fact came from, how it was transformed, and which sources contributed to the final result.
Evidence chain
Depending on the workload, provenance can preserve:
- Source system
- Original record identifier
- Canonical entity
- Retrieval path
- Transformation history
- Source authority
- Timestamps
- Intermediate tool outputs
- Model-generated inference
- Final conclusion
Conceptually:
- Source record
- Normalized record
- Canonical entity / relationship
- Retrieved evidence
- Tool or model interpretation
- Derived conclusion
The lineage does not imply that every internal implementation detail must be exposed publicly. It means the system can retain enough traceability to inspect important outputs.
Facts versus inferences
We preserve the distinction between the source says X and the system infers Y from X, Z, and additional evidence.
Those are different epistemic states. Collapsing them creates unverifiable model assertions and makes debugging substantially harder when an output is challenged.
Provenance through transformations
Company data is often transformed before use. Dates are normalized, entities are resolved, records are joined, transcripts are segmented, derived metrics are calculated.
The relevant lineage can therefore include not only the original source but also the transformations that produced the representation consumed downstream.
Operational value
Provenance supports evidence-backed answers, debugging, conflict investigation, auditability, evaluation, human review, and rollback analysis.
The objective is not to attach citations as decoration. It is to maintain an architecture where an important conclusion can be traced through an inspectable evidence chain rather than terminating at “the model said so.”
Provenance at the output boundary
The amount of lineage preserved can depend on consequence. A low-risk internal summary may need lightweight source references. A consequential financial, customer, or operating conclusion may warrant a more complete chain that identifies the records, calculations, transformations, and intermediate reasoning steps that contributed to it.
This also improves evaluation. When an answer is wrong, provenance helps distinguish whether the failure originated in ingestion, normalization, entity resolution, retrieval, source selection, calculation, or model reasoning. Without lineage, those failure classes collapse into one category: “the AI was wrong.”
Our architecture is designed to make the failure localizable, not merely observable.
Scope
This page is the evidence chain: keeping an answer attached to the records that support it, so a conclusion can be inspected rather than accepted on the system’s word.
Where that lineage is first recorded is Provenance & Source Authority in the data layer; which source wins when two of them conflict is Source Authority.