Product assumption traceability graph

01The offer

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.

For

Product discovery teams

Takes in

Approved discovery notes and requirement records

Delivers

Product assumption evidence graph

Message

Trace each product decision to its still-valid assumptions. Demonstrate the result with map one feature's evidence chain for product discovery teams. Use a concrete before-and-after example without promising unmeasured savings.

Lead magnet

Map one feature's evidence chain

02How it works

1. Extract stated assumptions
2. Link feature decisions
3. Attach supporting research
4. Flag contradictory findings
5. Track confidence reviews
6. Export evidence maps

Workflow

The buyer creates a project, supplies approved discovery notes and requirement records, and confirms scope and access. The working sequence is: 1. Extract stated assumptions. 2. Link feature decisions. 3. Attach supporting research. 4. Flag contradictory findings. 5. Track confidence reviews. 6. Export evidence maps. 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.

AI and people

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.

Screens

Key screens: Assumption graph, Evidence links, Review queue. 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.

Admin

Record ownership, access permissions, change proposals, original-value retention, version history, review dates, bulk import/export and duplicate resolution. Include organization-scoped access, named project owners, review queues, usage limits, export history and retention settings. Never reuse private customer material for other accounts without permission.

03Market gap

Alternatives buyers use today

Spreadsheets, shared folders, existing asset or information management systems and manual data cleanup. Position this concept around trace each product decision to its still-valid assumptions. Compare it against the customer's current process on the same representative task. This is proposed differentiation; no exhaustive competitor study or uniqueness claim has been established.

Where this wins

A useful niche taxonomy, customer-approved mappings and accumulated correction history that improve retrieval and reduce repeated cleanup. For this concept, accumulate permissioned examples and reviewer corrections around trace each product decision to its still-valid assumptions. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.

04Why now

Product Development teams are adopting AI for exactly this kind of repeatable work, and the cost of language and vision models has dropped far enough that a narrow, reviewed workflow pays back quickly. The buyer already feels the problem: requirements lose the research assumptions that originally justified them.

05Proof & signals

Channels where buyers gather: Product operations communities and discovery coaches. Metrics that prove it works: Unsupported decisions and retrieval time.

Paid pilot

Agree the acceptance criteria, input limits and reviewer responsibilities before starting. Run map one feature's evidence chain and deliver product assumption evidence graph. Compare unsupported decisions and retrieval time with the buyer's current process on comparable cases; include corrections, missed issues and reviewer time. Seek payment and repeat use. Stop or revise the scope if data access, accuracy or unit economics fail.

06Execution plan

MVP

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.

First 30 days

Week 1: interview five prospective buyers from product discovery teams and inspect how they handle requirements lose the research assumptions that originally justified them. Week 2: prepare map one feature's evidence chain using authorized or synthetic material. Week 3: share the demonstration through product operations communities and discovery coaches and seek one bounded paid pilot. Week 4: measure unsupported decisions and retrieval time, review delivery effort and ask for a repeat purchase. This is a validation schedule, not a promise that the full product can be built in thirty days.

After the pilot

After paying customers repeatedly accept product assumption evidence graph, automate flag contradictory findings; track confidence reviews; export evidence maps. Add one tested read integration, reusable customer configuration and scheduled repeat delivery. Increase supported formats or teams only when evaluation cases and reviewer capacity cover the new scope. One feature area; no invented confidence scores.

Retention

Build repeat use around product assumption evidence graph. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on unsupported decisions and retrieval time. Offer a recurring volume allowance after repeat demand; expand to adjacent tasks only when the buyer asks and delivery quality remains acceptable.

Integrations

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.

07Investment and running costs

Revenue model to test

Test USD 500-2,500 for one collection cleanup and launch, followed by USD 100-500 monthly for maintenance within agreed record limits. Larger migrations and complex rights management are separately scoped. Prices are hypotheses. For this buyer, package the first sale around map one feature's evidence chain and the defined product assumption evidence graph. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Cost drivers

Import cleanup, extraction, storage, indexing, steward review, duplicate investigation and recurring source updates. Initial validation additionally budgets for product researcher review. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.

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.