Public fact correction impact mapper
Correct the known spread of an error efficiently.
- For
- Communications teams managing owned channels
- Solves
- Teams cannot identify which downstream assets repeat a corrected fact.
- Delivers
- Editor-reviewed correction propagation map
- Built in
- about 4 weeks of creation time, MVP in 4 days
- Investment
- $20,500 for the MVP, $50,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
Correct the known spread of an error efficiently.
- Trace reused claim variants.
- Rank known distribution dependencies.
- Prepare targeted correction tasks.
- Compare the reviewed result with the recorded baseline and value assumptions.
- Capture corrections and named-owner approval before consequential use.
- Export a versioned editor-reviewed correction propagation map with source references and unresolved questions.
What goes in, what comes out
- Approved correction records
- Owned content graph
AI drafts, people review. Evidence-backed analysis and reporting workspace.
- Editor-reviewed correction propagation map
How it works
The workflow
- InStart with
Approved correction records and owned content graph
- 1
Confirm the buyer's problem and scope
- 2
Collect approved correction records and owned content graph
- 3
Then follow this sequence: 1
- OutFinish with
Editor-reviewed correction propagation map
AI does the heavy lifting, people stay in charge
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. No automated external takedowns or unsupported reach estimates. A model suggestion is never a verified fact, professional decision or authorization to act.
What your team sees
Primary screens: Data and definitions, Pattern investigation, Action and value review. 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. Make the task-specific outcome editor-reviewed correction propagation map visible beside its evidence, review state and value baseline.
Accounts and administration
Dataset permissions, field mappings, metric definitions, source drill-down, saved filters, reviewer annotations, recurring reports and action ownership. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.
Integrations and data access
Approved company facts, permitted media sources and publication workflows. Read-only business data exports, reporting databases and task trackers. Reconcile source totals before scheduling recurring data refreshes. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
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
4 daysOne buyer segment, one recurring use case; first modules: trace reused claim variants; rank known distribution dependencies. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
5 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
9 daysSelf-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 communications teams managing owned channels use it to solve "teams cannot identify which downstream assets repeat a corrected fact"?
- 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 quality and outcome thresholds before the pilot using this measure: Uncorrected owned copies and reviewer hours per verified correction.
- Measure, then decide. Track uncorrected owned copies and reviewer hours per verified correction; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.
MVP scope for this solution. Pilot scope: No automated external takedowns or unsupported reach estimates. Implement one approved input format, a bounded representative case set and the first two task modules: trace reused claim variants; rank known distribution dependencies. Support the third module with operator review: prepare targeted correction tasks. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around editor-reviewed correction propagation map. Retain the explicit scope boundary: No automated external takedowns or unsupported reach estimates.
What the build depends on. Stable identifiers, consistent metric definitions, deterministic calculations, source lineage and representative review samples. Poor coverage must remain visible. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: No automated external takedowns or unsupported reach estimates.
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: trace reused claim variants; rank known distribution dependencies. 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$50,000about 4 weeks of creation time · start with the MVP from $20,500
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 | $30–$60 | $80–$160 | $110–$220 |
| Full productabout 50 customers | $110–$210 | $880–$1,750 | $990–$1,960 |
Run it or resell it
For your own team
Communications teams managing owned channels run it inside the business: approved correction records and owned content graph in, editor-reviewed correction propagation map 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
#7a2791 - accent
#79c954 - surface
#eee4f1 - ink
#22201e
- Headings
- Sora
- Text
- Work Sans
- Voice
- Articulate, timely, composed
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. Package the initial sale as one bounded editor-reviewed correction propagation map. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.
Message to test
Correct the known spread of an error efficiently. Demonstrate a concrete editor-reviewed correction propagation map using the buyer's approved example and show the baseline, corrections and actual delivery effort.
Where to find buyers
Communications teams managing owned channels professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.
Lead magnet
A reviewed sample editor-reviewed correction propagation map from a small authorized input set, with a transparent calculation of uncorrected owned copies and reviewer hours per verified correction and no promised savings.
The first 30 days
- Week 1: interview five communications teams managing owned channels and inspect a recent example of teams cannot identify which downstream assets repeat a corrected fact.
- Week 2: prepare a consented or synthetic demonstration of the three task modules.
- Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
- Week 4: measure uncorrected owned copies and reviewer hours per verified correction, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.
Paid pilot
Agree quality and outcome thresholds before the pilot using this measure: Uncorrected owned copies and reviewer hours per verified correction. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.
Success metrics
Uncorrected owned copies and reviewer hours per verified correction; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.
Retention and expansion
Repeat the workflow when the buyer again needs editor-reviewed correction propagation map. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.
Why clients would pick it
Domain-specific definitions, trusted source mappings and a history connecting findings to actions and observed results. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for communications teams managing owned channels. Repeatable delivery and useful integrations matter more than access to a base model.
Alternatives and positioning
Analysts, business intelligence dashboards, spreadsheets and general text summarization tools. Compare this product with the buyer's present method on uncorrected owned copies and reviewer hours per verified correction. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.
Main delivery costs
Data preparation, reconciliation, classification, expert interpretation, customer-specific definitions and recurring reporting support. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of editor-reviewed correction propagation map. Track cost per accepted output, including correction work, unsuccessful cases and support.
Safeguards
Verify public facts and quotations. Keep publication authority explicit and preserve the original context behind media and reputation findings. No automated external takedowns or unsupported reach estimates. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.