Feature discovery evidence notebook

For product growth teams, turn consented research and aggregate interaction records into reviewed discovery evidence brief. Address this specific problem: feature awareness is inferred from usage alone. The aim: distinguish not knowing from not needing. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.

The buyer creates a project, supplies consented research and aggregate interaction records, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final reviewed discovery evidence brief before use. Retain source links and a version history for the next cycle.

How the AI works

AI assists these bounded tasks: separate awareness evidence; compare access barriers; draft follow-up questions. Use only consented research and aggregate interaction records and preserve uncertainty in reviewed discovery evidence brief. 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 organization, one defined input format and one representative pilot batch using consented research and aggregate interaction records. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. 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 organization, one defined input format and one representative pilot batch using consented research and aggregate interaction records. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: separate awareness evidence; compare access barriers. Support the third task through an assisted review queue: draft follow-up questions. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of reviewed discovery evidence brief. 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. Read-only business data exports, reporting databases and task trackers. Reconcile source totals before scheduling recurring data refreshes. Begin with uploads and exports of consented research and aggregate interaction 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

Open with a compact overview and filters for the relevant period or segment. Let users drill from each theme or metric into underlying records. Keep source definitions and missing-data notes near the result. Use an action panel to assign investigations and record what was learned. Open with brief and sources; move into feature discovery evidence notebook for the detailed task; finish in review and delivery for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.