Evidence-backed text origin review workspace

For

Educators, admissions reviewers and editors checking whether written text was AI-generated or human-written

Solves

Single-score detectors give no evidence trail, so reviewers cannot defend a decision or compare cases consistently.

Delivers

Reviewer-signed text-origin evidence reports linked to the reviewed submission

Built in

about 4 weeks of creation time, MVP in 4 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 disputed text-origin decisions while preserving a defensible evidence record.

1. Detect whether supplied text is AI-generated or human-written.
2. Produce a calibrated likelihood score with stated uncertainty.
3. Highlight suspected sentences for detailed review.
4. Generate a structured report with signal breakdowns and insights.
5. Scan pasted or typed text in real time.
6. Accept pasted text, typed text, URLs and uploaded files.
7. Process batch uploads of multiple documents.
8. Expose an API for existing review systems.
9. Check for copied or unoriginal content alongside AI detection.
10. Trace text to likely AI source or origin.
11. Suggest humanization edits for teaching feedback.
12. Flag attempts to disguise AI text as human-written.
13. Certify authorship as human, AI or mixed.
14. Allow adjustable sensitivity per policy.
15. Show colored indicators for quick triage.
16. Combine multiple detection algorithms into one result.
17. Support immediate use without account creation.
18. Offer a free basic tier for low-stakes checks.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner sign-off before consequential use.
21. Export a versioned reviewer-signed text-origin evidence report with source references and unresolved questions.

Everything these tools do, in one app

• AI content detection Analyzes text to determine whether it was generated by AI or written by a human.Found in AI Detector, Wordvice AI Detector, GPTZero and 6 more
• Likelihood score Provides a quantitative score or percentage indicating how likely the text is AI-generated.Found in GPTZero, Polygraf AI, AICheatCheck
• Sentence highlighting Highlights specific sentences suspected to be AI-generated for detailed review.Found in GPTZero
• Detailed reports Generates reports with breakdowns and insights about the analyzed content.Found in GPTKit, Free AI Detector, AICheatCheck
• Real-time scanning Scans content instantly as it is entered or browsed to provide immediate feedback.Found in Free AI Detector, Detect GPT, Polygraf AI
• Multiple input methods Allows users to paste text, type directly, enter a URL, or upload files for analysis.Found in Free AI Detector, Detect GPT
• Batch uploads Enables analysis of multiple documents at once for efficiency.Found in GPTZero
• API access Offers API integration for incorporating detection into existing systems or workflows.Found in GPTZero
• Plagiarism detection Checks for copied or unoriginal content alongside AI detection.Found in Free AI Detector, Polygraf AI
• Source identification Traces text back to its potential AI source or origin.Found in Polygraf AI
• Humanization suggestions Provides recommendations to make AI-generated text appear more human-like.Found in Polygraf AI
• Deception filter Detects attempts to disguise AI-generated text as human-written.Found in Polygraf AI
• Authorship certification Verifies and certifies whether content is human-written, AI-generated, or both.Found in authentiGPT
• Adjustable sensitivity Allows users to tailor the verification sensitivity to specific needs.Found in authentiGPT
• Visual indicators Uses colored icons or signals to quickly show if content is AI-generated.Found in Detect GPT
• Multi-algorithm analysis Combines multiple detection techniques or models to improve accuracy.Found in AI Detector, GPTKit
• No signup required Allows immediate use without creating an account.Found in AI Detector
• Free access Offers free usage options for basic detection needs.Found in AI Detector, GPTKit, authentiGPT and 1 more

What goes in, what comes out

The workflow

1. In
   Start with

   Submitted documents, author statements, permitted source material and review policy

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect submitted documents

4. 3

   Author statements

5. 4

   Permitted source material and review policy

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-signed text-origin evidence reports linked to the reviewed submission

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. Detection signals are probabilistic; final origin judgments and academic consequences remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Submission intake and policy, Editable review workspace, Signed report and delivery. Use a thumbnail gallery for submissions, a large central text canvas with sentence-level highlighting, and a right-hand panel for signals, sources and comments. Let users compare detector runs side by side. Display draft, changes requested and signed states. Provide a shareable report link with comments anchored to the relevant passage. Make the task-specific outcome reviewer-signed text-origin evidence reports linked to the reviewed submission visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, submission versions, reviewer comments, sign-off states, usage allowances, review 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

Institution-owned submission systems, authorized author statements and permitted research sources. Cloud document storage, learning-management-system import/export and reporting 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

   4 days

   One buyer segment, one recurring use case; first modules: detect whether supplied text is AI-generated or human-written; produce a calibrated likelihood score with stated uncertainty. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

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

4. 4

   Full product

   10 days

   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 educators, admissions reviewers and editors checking whether written text was AI-generated or human-written use it to solve "single-score detectors give no evidence trail, so reviewers cannot defend a decision or compare cases consistently"?
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: Reviewer-agreed origin decisions per review hour and upheld decisions after appeal.
4. Measure, then decide. Track reviewer-agreed origin decisions per review hour and upheld decisions after appeal; 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 review policy and one document type; final origin judgments and academic consequences remain human. Implement one approved input format, a bounded representative case set and the first two task modules: detect whether supplied text is AI-generated or human-written; produce a calibrated likelihood score with stated uncertainty. Support the third module with operator review: highlight suspected sentences for detailed review. Include source references, corrections, basic organization access, sign-off 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-signed text-origin evidence reports linked to the reviewed submission. Retain the explicit scope boundary: One review policy and one document type; final origin judgments and academic consequences remain human.

What the build depends on. Document upload and preview, asynchronous detection jobs, editable version history, reviewer access and tested export formats. High-fidelity review requires qualified academic reviewers. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One review policy and one document type; final origin judgments and academic consequences 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 review policy. Offer a monthly review allowance after repeat demand. Quote complex institutional integrations or specialist appeals support separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-signed text-origin evidence report. 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 disputed text-origin decisions while preserving a defensible evidence record. Demonstrate a concrete reviewer-signed text-origin evidence report using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Educators, admissions reviewers and editors professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-signed text-origin evidence report from a small authorized input set, with a transparent calculation of reviewer-agreed origin decisions per review hour and upheld decisions after appeal and no promised savings.

The first 30 days

1. Week 1: interview five educators, admissions reviewers and editors checking whether written text was AI-generated or human-written and inspect a recent example of single-score detectors give no evidence trail, so reviewers cannot defend a decision or compare cases consistently.
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 reviewer-agreed origin decisions per review hour and upheld decisions after appeal, 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: Reviewer-agreed origin decisions per review hour and upheld decisions after appeal. 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

Reviewer-agreed origin decisions per review hour and upheld decisions after appeal; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-signed text-origin evidence reports linked to the reviewed submission. 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 review policies, calibration cases and reviewer corrections, together with reliable delivery for a narrow academic niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for educators, admissions reviewers and editors checking whether written text was AI-generated or human-written. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

AI Detector, Wordvice AI Detector, GPTZero, GPTKit, Free AI Detector, Polygraf AI, authentiGPT, Alta 2.0, Detect GPT and AICheatCheck. Compare this product with the buyer's present method on reviewer-agreed origin decisions per review hour and upheld decisions after appeal. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Detection runs, storage, reviewer hours, appeal handling and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-signed text-origin evidence reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve author voice, source attribution, quotation accuracy and usage permissions. Reviewers approve substantive origin judgments and academic consequences. One review policy and one document type; final origin judgments and academic consequences remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.