{"slug":"product-assumption-traceability-graph","name":"Product assumption traceability graph","category":"Product Development","customer":"Product discovery teams","problem":"Requirements lose the research assumptions that originally justified them.","value":"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.","format":"Searchable structured library and data stewardship console","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.","functionality":"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":"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.","inputs":"Approved discovery notes and requirement records","deliverables":"Product assumption evidence graph","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.","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.","expansion":"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.","usp":"Trace each product decision to its still-valid assumptions.","defensibility":"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.","alternatives":"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.","revenue":"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.","costs":"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.","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.","dependencies":"Stable identifiers, an agreed data schema, reversible imports, mapping review and source ownership. Data quality work can exceed model development effort. Obtain representative authorized inputs, an agreed review rubric and a buyer-side owner. Specific scope: One feature area; no invented confidence scores.","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.","plan30":"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.","metrics":"Unsupported decisions and retrieval time","channels":"Product operations communities and discovery coaches","leadMagnet":"Map one feature's evidence chain","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.","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.","controls":"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.","crossSector":"Customer Support; Science and Research","fn":["Extract stated assumptions","Link feature decisions","Attach supporting research","Flag contradictory findings","Track confidence reviews","Export evidence maps"],"sc":{"opp":8,"pain":7,"feas":7,"now":8},"phases":[{"name":"MVP","scope":"One buyer segment, one recurring use case; first modules: extract stated assumptions; link feature decisions. Manual review in the loop.","time":{"days":4,"label":"4 days"},"usd":14500},{"name":"Paid pilot","scope":"Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.","time":{"days":5,"label":"5 days"},"usd":14500},{"name":"Full product","scope":"Self-serve onboarding, billing, monitoring and the wider integration set.","time":{"days":10,"label":"10 days"},"usd":20500}],"running":[{"stage":"MVP and paid pilot","note":"about 3 customers","hosting":[30,60],"ai":[50,100],"total":[80,160]},{"stage":"Full product","note":"about 50 customers","hosting":[110,210],"ai":[350,700],"total":[460,910]}],"total":49500,"complexity":0.45,"days":19,"shot":true,"demo":true}