Product assumption traceability graph

For product discovery teams, turn approved discovery notes and requirement records into product assumption evidence graph. Address this specific problem: requirements lose the research assumptions that originally justified them. The aim: trace each product decision to its still-valid assumptions. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.

The buyer creates a project, supplies approved discovery notes and requirement records, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final product assumption evidence graph before use. Retain source links and a version history for the next cycle.

How the AI works

Suggest evidence relationships with cited passages. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.

Safeguards

Use consented research and preserve contradictory evidence. Separate observed user behavior, proposed explanations and untested product assumptions. One feature area; no invented confidence scores. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.

What to build first

Costed pilot: One feature area; no invented confidence scores. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract stated assumptions; link feature decisions. Support the third task through an assisted review queue: attach supporting research. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of product assumption evidence graph. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.

What it can connect to

Product feedback, authorized interviews, usage exports and requirement records. Source systems, catalog exports and cloud file storage. Start with reversible CSV or file imports and validate identifiers before any direct writes. Begin with uploads and exports of approved discovery notes and requirement records. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.

The screens in detail

Use a searchable table or visual gallery with filters for the domain’s important attributes. Open each item into a detail drawer containing source records, ownership and history. Put proposed merges and field changes in a separate review queue. Provide a preview before any bulk export. Open with assumption graph; move into evidence links for the detailed task; finish in review queue for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.