
Board metric definition registry
A versioned meaning for each board-level metric.
- For
- Finance teams producing board packs
- Solves
- Revenue and margin labels mean different things across reports.
- Delivers
- Approved board metric dictionary
- Built in
- about 5 weeks of creation time, MVP in 6 days
- Investment
- $11,000 for the MVP, $37,500 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
For finance teams producing board packs, turn approved metric definitions and reporting examples into approved board metric dictionary.
- Extract metric formulas.
- Link source fields.
- Flag inconsistent labels.
- Record approved definitions.
- Track historical changes.
- Export reporting dictionary.
What goes in, what comes out
- Approved metric definitions
- Reporting examples
AI drafts, people review. Searchable structured library and data stewardship console.
- Approved board metric dictionary
How it works
The workflow
- InStart with
Approved metric definitions and reporting examples
- 1
The buyer creates a project
- 2
Supplies approved metric definitions and reporting examples
- 3
Confirms scope and access
- OutFinish with
Approved board metric dictionary
AI does the heavy lifting, people stay in charge
Compare semantic definitions with deterministic formula review. 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.
What your team sees
Key screens: Metric catalog, Definition comparison, Change history. 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 metric catalog; move into definition comparison for the detailed task; finish in change history for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.
Accounts and administration
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.
Integrations and data access
Accounting exports, invoice records and finance review processes. 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 metric definitions and reporting examples. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
How we build it
We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.
- 1
Scoping call
Day 1Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.
- 2
MVP
6 daysOne buyer segment, one recurring use case; first modules: extract metric formulas; link source fields. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
7 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
3 weeksSelf-serve onboarding, billing, monitoring and the wider integration set.
- 5
Run and improve
MonthlyWe host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.
Why we start with an MVP
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.
- Pick the riskiest assumption. Here: will finance teams producing board packs use it to solve "revenue and margin labels mean different things across reports"?
- Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
- Run a paid pilot. Agree the acceptance criteria, input limits and reviewer responsibilities before starting.
- Measure, then decide. Track definition disputes and report corrections. Then expand, change course or stop, with evidence instead of opinions.
MVP scope for this solution. Costed pilot: Definitions only; underlying reporting pipeline excluded. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract metric formulas; link source fields. Support the third task through an assisted review queue: flag inconsistent labels. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of approved board metric dictionary. 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 approved board metric dictionary, automate record approved definitions; track historical changes; export reporting dictionary. 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. Definitions only; underlying reporting pipeline excluded.
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: Definitions only; underlying reporting pipeline excluded.
Investment
A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.
- Phase 1
MVP
One buyer segment, one recurring use case; first modules: extract metric formulas; link source fields. Manual review in the loop.
- Phase 2
Paid pilot
Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- Phase 3
Full product
Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$37,500about 5 weeks of creation time · start with the MVP from $11,000
Running costs per month
A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.
| Stage | Hosting and infrastructure | AI usage | Total per month |
|---|---|---|---|
| MVP and paid pilotabout 3 customers | $50–$100 | $50–$100 | $100–$200 |
| Full productabout 50 customers | $190–$380 | $350–$700 | $540–$1,080 |
Run it or resell it
For your own team
Finance teams producing board packs run it inside the business: approved metric definitions and reporting examples in, approved board metric dictionary out, reviewed by your people.
As part of your offer
Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.
Your brand, or this one
Run it under your own brand, or start from this concept style.
- primary
#5f9127 - accent
#8b54c9 - surface
#ebf1e4 - ink
#22201e
- Headings
- Sora
- Text
- Work Sans
- Voice
- Exact, sober, trustworthy
Selling it to your own clients: the go-to-market playbook
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 reconcile ten recurring metrics and the defined approved board metric dictionary. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.
Message to test
A versioned meaning for each board-level metric. Demonstrate the result with reconcile ten recurring metrics for finance teams producing board packs. Use a concrete before-and-after example without promising unmeasured savings.
Where to find buyers
Fractional CFOs and reporting consultants
Lead magnet
Reconcile ten recurring metrics
The first 30 days
- Week 1: interview five prospective buyers from finance teams producing board packs and inspect how they handle revenue and margin labels mean different things across reports.
- Week 2: prepare reconcile ten recurring metrics using authorized or synthetic material.
- Week 3: share the demonstration through fractional CFOs and reporting consultants and seek one bounded paid pilot.
- Week 4: measure definition disputes and report corrections, 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 reconcile ten recurring metrics and deliver approved board metric dictionary. Compare definition disputes and report corrections 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
Definition disputes and report corrections
Retention and expansion
Build repeat use around approved board metric dictionary. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on definition disputes and report corrections. 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 a versioned meaning for each board-level metric. 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 a versioned meaning for each board-level metric. 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 finance definition workshop. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.
Safeguards
Reconcile calculations to approved records. Keep proposed entries and payment actions under finance-team control. Never invent missing financial inputs. Definitions only; underlying reporting pipeline excluded. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.