Finance spreadsheet assumption index

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will finance modeling teams use it to solve "key assumptions are buried across spreadsheet files"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree the acceptance criteria, input limits and reviewer responsibilities before starting.
4. Measure, then decide. Track unsourced material assumptions; reviewer correction minutes; buyer acceptance and repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Costed pilot: One organization, one defined input format and one representative pilot batch using authorized spreadsheets and model documentation. 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: locate declared assumptions; link source notes. Support the third task through an assisted review queue: flag unexplained changes. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of model assumption index. 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.

After the MVP. After paying customers repeatedly accept model assumption index, automate link proposed outputs to original source records; capture reviewer corrections and approval; export a versioned model assumption index. 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 organization, one defined input format and one representative pilot batch using authorized spreadsheets and model documentation. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.

What the build depends on. 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 organization, one defined input format and one representative pilot batch using authorized spreadsheets and model documentation. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.

Pricing 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 prepare a sample model assumption index from a small authorized set of authorized spreadsheets and model documentation and the defined model assumption index. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Message to test

Find the reasoning behind model inputs. Demonstrate the result with prepare a sample model assumption index from a small authorized set of authorized spreadsheets and model documentation for finance modeling teams. Use a concrete before-and-after example without promising unmeasured savings.

Where to find buyers

Accounting practices and controller communities

Lead magnet

Prepare a sample model assumption index from a small authorized set of authorized spreadsheets and model documentation

The first 30 days

1. Week 1: interview five prospective buyers from finance modeling teams and inspect how they handle key assumptions are buried across spreadsheet files.
2. Week 2: prepare prepare a sample model assumption index from a small authorized set of authorized spreadsheets and model documentation using authorized or synthetic material.
3. Week 3: share the demonstration through accounting practices and controller communities and seek one bounded paid pilot.
4. Week 4: measure unsourced material assumptions; reviewer correction minutes; buyer acceptance and repeat purchase, 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.

Paid pilot

Agree the acceptance criteria, input limits and reviewer responsibilities before starting. Run prepare a sample model assumption index from a small authorized set of authorized spreadsheets and model documentation and deliver model assumption index. Compare unsourced material assumptions; reviewer correction minutes; buyer acceptance and repeat purchase 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.

Success metrics

Unsourced material assumptions; reviewer correction minutes; buyer acceptance and repeat purchase

Retention and expansion

Build repeat use around model assumption index. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on unsourced material assumptions; reviewer correction minutes; buyer acceptance and repeat purchase. Offer a recurring volume allowance after repeat demand; expand to adjacent tasks only when the buyer asks and delivery quality remains acceptable.

Why clients would pick it

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 find the reasoning behind model inputs. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.

Alternatives and positioning

Spreadsheets, shared folders, existing asset or information management systems and manual data cleanup. Position this concept around find the reasoning behind model inputs. 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.

Main delivery costs

Import cleanup, extraction, storage, indexing, steward review, duplicate investigation and recurring source updates. Initial validation additionally budgets for representative sample preparation, interviews with finance modeling teams, and qualified domain review of model assumption index. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.