Garbage in, garbage out. What is the lineage of the "org context" your AI reasons over — where does each input come from, and how would I trace an output back to its inputs?

The full executive answer

The context has a defined pipeline, not a data swamp. Layer by layer: the system identity is fixed text; the organisation context block is built fresh from your database records — your assessment answers, profile, scores — by a single server-side function; the task instruction is per-module template; and retrieval snippets come from documents you uploaded, each chunk stored with its source document identity. Nothing enters a prompt from an untyped side channel, and anything user-written or external passes the neutralisation and PII scrub first.

Traceability runs in both directions. Forward: every stored insight records which model produced it, which provider, token counts, which documents grounded it, and which frameworks it cited. Backward: the caching key is a fingerprint of the exact inputs — organisation context, module, prompt version, model, and the state of your document knowledge base. That last part matters: upload or change a document and the fingerprint changes, which automatically invalidates stale cached analyses. Your data changing forces the conclusions to be recomputed — lineage with teeth.

And we grade our own inputs honestly, which most platforms do not: the Evidence Map classifies every assessment dimension as corroborated, supported, self-reported, or unevidenced, and that classification mechanically adjusts the confidence band on outputs — unresolved conflicts between self-assessment and independent signals widen the band downward and are stamped on the output. Where the current state ends: this is application-level lineage, purpose-built for this pipeline; it is not a general enterprise lineage graph with column-level tracking, and I would not sell it as one.