Claim verification and source evidence workbench

For

Writers, editors and research teams who must verify claims before publication

Solves

Claims arrive from drafts, links and AI outputs, and checking them against credible sources is slow, inconsistent and hard to audit.

Delivers

Reviewer-approved verification reports with source references

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce verification time while keeping a defensible evidence trail.

1. Accept claims as links, long text or AI-generated responses.
2. Check each claim against credible sources.
3. Cross-reference multiple sources to validate or refute.
4. Retrieve current information for real-time analysis.
5. Let users explore the sources behind each check.
6. Flag dubious sources, logical flaws and missing context.
7. Show confidence scores when sources cannot fully validate a claim.
8. Scan text for errors, bias and subjective language.
9. Check originality and flag copied passages.
10. Rephrase text while preserving meaning, with selectable modes.
11. Tag entries as Confirmed, Pending or Debunked.
12. Produce detailed explanation reports with evidence.
13. Keep verification history local to the browser.
14. Embed checks in chat interfaces and browser pages.
15. Allow crowd-sourced additions and moderation.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-owner approval before consequential use.
18. Export a versioned reviewer-approved verification report with source references and unresolved questions.

Everything these tools do, in one app

• Claim verification Checks the accuracy of a statement or claim against reliable sources.Found in Stepfun Diligence Check, FactSnap, Pino - Fact Checker and 4 more
• Multi-source cross-referencing Compares claims against multiple credible sources to validate information.Found in Stepfun Diligence Check, FactSnap, Pino - Fact Checker and 2 more
• Real-time analysis Provides up-to-date verification using current information rather than outdated data.Found in Stepfun Diligence Check, FactSnap, Pino - Fact Checker and 2 more
• Source exploration Allows users to dive deeper into the sources behind each fact-check for thorough assessment.Found in FactSnap, Pino - Fact Checker, Verol and 2 more
• AI-powered agents Uses AI to identify dubious sources, logical flaws, and missing context in claims.Found in Stepfun Diligence Check, Pino - Fact Checker, Snopes Factbot and 2 more
• Multiple input types Supports verifying various content types such as links, long texts, and AI-generated responses.Found in Stepfun Diligence Check, Pino - Fact Checker, Factiverse
• User-friendly interface Simplifies the fact-checking process with an easy-to-use interface.Found in Stepfun Diligence Check, FactSnap, Pino - Fact Checker and 1 more
• Browser integration Integrates seamlessly into web browsers for convenient fact-checking while browsing.Found in FactSnap, Pino - Fact Checker
• Chat integration Embeds fact-checking directly within chat interfaces like ChatGPT for seamless workflow.Found in Factiverse plugin for ChatGPT, Verol
• Confidence metrics Displays confidence scores when sources cannot fully validate a claim, indicating uncertainty.Found in Verol
• Local data storage Keeps verification history local within the browser to avoid external data tracking.Found in Verol
• Error and bias detection Scans text for mistakes, biases, and subjective language.Found in Factiverse
• Detailed explanation reports Provides clear evidence reports supporting or refuting the information checked.Found in Pino - Fact Checker, Springfield Oracle
• Status tagging Labels entries with clear statuses such as Confirmed, Pending, or Debunked.Found in Springfield Oracle
• Community contributions Allows crowd-sourced additions and moderation to grow and audit the database.Found in Springfield Oracle
• Educational value Serves as an informative resource for users curious about the origins and truth behind claims.Found in Snopes Factbot
• Plagiarism detection Checks for originality of output to ensure content is not copied.Found in Parafact AI
• Paraphrasing engine Rephrases text while preserving original meaning, with multiple rewriting modes.Found in Parafact AI

What goes in, what comes out

The workflow

1. In
   Start with

   Submitted claims, source links, AI-generated text and licensed source material

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect submitted claims

4. 3

   Source links and AI-generated text

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewer-approved verification reports with source references

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed source policy and licensed source set; final editorial and legal checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Claim intake and source setup, Verification workspace, Evidence report and delivery. Use a queue of submitted claims, a large central claim view with source excerpts, and a right-hand panel for status, confidence and reviewer comments. Let users compare supporting and refuting sources side by side. Display Confirmed, Pending and Debunked states. Provide a shareable report link with comments anchored to the relevant claim. Make the task-specific outcome reviewer-approved verification reports with source references visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, claim versions, reviewer comments, approval states, source allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Author-owned drafts, authorized source databases and permitted research sources. Cloud storage, browser extensions, chat interfaces and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

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. 1

   Scoping call

   Day 1

   Thirty 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. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: accept claims as links, long text or AI-generated responses; check each claim against credible sources. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We 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.

1. Pick the riskiest assumption. Here: will writers, editors and research teams who must verify claims before publication use it to solve "claims arrive from drafts, links and AI outputs, and checking them against credible sources is slow, inconsistent and hard to audit"?
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 quality and outcome thresholds before the pilot using this measure: Verified claims per editorial hour and corrections after publication.
4. Measure, then decide. Track verified claims per editorial hour and corrections after publication; 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: One fixed source policy and licensed source set; final editorial and legal checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: accept claims as links, long text or AI-generated responses; check each claim against credible sources. Support the third module with operator review: cross-reference multiple sources to validate or refute. 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 reviewer-approved verification reports with source references. Retain the explicit scope boundary: One fixed source policy and licensed source set; final editorial and legal checks remain human.

What the build depends on. Claim upload and preview, asynchronous verification jobs, editable version history, reviewer access and tested export formats. High-fidelity verification requires specialist editorial QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed source policy and licensed source set; final editorial and legal checks remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

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.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined claim package. Offer a monthly verification allowance after repeat demand. Quote complex legal or regulated review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved verification report with source references. 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

Reduce verification time while keeping a defensible evidence trail. Demonstrate a concrete reviewer-approved verification report with source references using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Writers, editors and research teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved verification report with source references from a small authorized input set, with a transparent calculation of verified claims per editorial hour and corrections after publication and no promised savings.

The first 30 days

1. Week 1: interview five writers, editors and research teams who must verify claims before publication and inspect a recent example of claims arriving from drafts, links and AI outputs, and checking them against credible sources being slow, inconsistent and hard to audit.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure verified claims per editorial hour and corrections after publication, 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: Verified claims per editorial hour and corrections after publication. 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

Verified claims per editorial hour and corrections after publication; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved verification reports with source references. 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

A reusable library of approved source policies, verification examples and reviewer corrections, together with reliable delivery for a narrow editorial niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for writers, editors and research teams who must verify claims before publication. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Stepfun Diligence Check, FactSnap, Parafact AI, Verol, Pino - Fact Checker, AVI by True AI, Snopes Factbot, Factiverse plugin for ChatGPT, Factiverse and Springfield Oracle. Compare this product with the buyer's present method on verified claims per editorial hour and corrections after publication. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Source retrieval, model calls, storage, reviewer hours, client revision rounds and licensed source access. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved verification reports with source references. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Editors approve substantive changes and publication scope. One fixed source policy and licensed source set; final editorial and legal checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.