Open data reporting service
Industry-specific interpretation with transparent dataset definitions.
- For
- Trade associations monitoring regional business activity
- Solves
- Members cannot interpret fragmented public datasets.
- Delivers
- Source-linked industry data briefs
- Built in
- about 5 weeks of creation time, MVP in 6 days
- Investment
- $11,000 for the MVP, $44,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
For trade associations monitoring regional business activity, turn official datasets, metadata and update schedules into source-linked industry data briefs.
- Validate dataset definitions.
- Harmonize periods.
- Chart comparable trends.
- Explain limitations.
- Schedule refreshes.
- Export member briefs.
What goes in, what comes out
- Official datasets
- Metadata
- Update schedules
AI drafts, people review. Evidence-backed analysis and reporting workspace.
- Source-linked industry data briefs
How it works
The workflow
- InStart with
Official datasets, metadata and update schedules
- 1
Agree definitions
- 2
Import authorized data
- 3
Validate coverage and identifiers
- 4
Compute transparent measures
- 5
Group relevant evidence
- 6
Review findings
- 7
Assign investigations or improvements
- 8
Repeat on a comparable period
- OutFinish with
Source-linked industry data briefs
AI does the heavy lifting, people stay in charge
Classify text, summarize evidence and propose explanations to investigate. Compute financial or operational measures with deterministic code. Separate observed patterns from causal claims and preserve examples that contradict the summary.
What your team sees
Key screens: Dataset catalog, trend explorer, member reports. 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. In this product, the first view is dataset catalog, followed by trend explorer and member reports.
Accounts and administration
Dataset permissions, field mappings, metric definitions, source drill-down, saved filters, reviewer annotations, recurring reports and action ownership.
Integrations and data access
Official publications, agency document stores and approved service workflows. Read-only business data exports, reporting databases and task trackers. Reconcile source totals before scheduling recurring data refreshes. These are candidate integration categories, not verified supported connectors.
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: validate dataset definitions; harmonize periods. 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 weeksRemaining modules: explain limitations; schedule refreshes; export member briefs. Self-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 trade associations monitoring regional business activity use it to solve "members cannot interpret fragmented public datasets"?
- 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. Analyze one historical period and review findings with the responsible domain owner.
- Measure, then decide. Track refresh accuracy and member report usage. Then expand, change course or stop, with evidence instead of opinions.
MVP scope for this solution. Begin with trade associations monitoring regional business activity and one recurring use case. Build the first two modules: validate dataset definitions; harmonize periods. Provide operator assistance for the third module: chart comparable trends. Deliver source-linked industry data briefs through a manual review queue. Perform other necessary full-scope functions manually during the pilot. Include all applicable access, accuracy and professional-review controls from the start.
After the MVP. After paid pilots establish value, automate the remaining modules: explain limitations; schedule refreshes; export member briefs. Add one validated source integration, reusable customer configuration and recurring delivery. Expand to additional teams, document formats or languages only after testing the new scope.
What the build depends on. Stable identifiers, consistent metric definitions, deterministic calculations, source lineage and representative review samples. Poor coverage must remain visible.
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: validate dataset definitions; harmonize periods. 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
Remaining modules: explain limitations; schedule refreshes; export member briefs. Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$44,000about 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 | $80–$160 | $130–$260 |
| Full productabout 50 customers | $190–$380 | $880–$1,750 | $1,070–$2,130 |
Run it or resell it
For your own team
Trade associations monitoring regional business activity run it inside the business: official datasets, metadata and update schedules in, source-linked industry data briefs 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
#4a9127 - accent
#ba54c9 - surface
#e9f1e4 - ink
#22201e
- Headings
- Playfair Display
- Text
- Source Sans 3
- Voice
- Plain-spoken, neutral, accountable
Selling it to your own clients: the go-to-market playbook
Pricing to test
Test USD 500-2,000 for an initial analysis of one bounded dataset. Offer USD 250-1,000 monthly for repeat reporting at agreed volume. Data cleanup and specialist analysis are separately priced. These are test ranges.
Message to test
Open data reporting service for trade associations monitoring regional business activity. Industry-specific interpretation with transparent dataset definitions. Demonstrate the claim through a regional industry report from named public datasets.
Where to find buyers
Trade associations and regional chambers
Lead magnet
A regional industry report from named public datasets
The first 30 days
- Week 1: interview five prospective buyers in this segment: trade associations monitoring regional business activity. Ask to see a recent example of the problem and their current process.
- Week 2: prepare this demonstration using authorized or synthetic material: a regional industry report from named public datasets.
- Week 3: present it through trade associations and regional chambers and seek one narrowly scoped paid pilot.
- Week 4: review refresh accuracy, member report usage, total delivery effort and a concrete renewal decision before increasing scope.
Paid pilot
Analyze one historical period and review findings with the responsible domain owner. Reconcile headline measures, inspect counterexamples and ask the buyer to choose a concrete follow-up action. For this solution, use official datasets, metadata and update schedules and evaluate source-linked industry data briefs. Agree success thresholds with the buyer before starting; collect a baseline for refresh accuracy, member report usage. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.
Success metrics
Refresh accuracy, member report usage
Retention and expansion
Repeat the same definitions each reporting period and track whether findings lead to useful action. Expand data sources without breaking historical comparability.
Why clients would pick it
Domain-specific definitions, trusted source mappings and a history connecting findings to actions and observed results. For this solution, build around industry-specific interpretation with transparent dataset definitions. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.
Alternatives and positioning
Analysts, business intelligence dashboards, spreadsheets and general text summarization tools. Differentiate on this specific proposed advantage: industry-specific interpretation with transparent dataset definitions. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.
Main delivery costs
Data preparation, reconciliation, classification, expert interpretation, customer-specific definitions and recurring reporting support.
Safeguards
Preserve official source versions, accessibility and audit records. Confirm agency-specific procurement, records and data handling requirements during discovery. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.