Multi-format synthetic content evidence workbench

For

Trust, safety and verification teams reviewing images, audio, video and messages

Solves

Reviewers cannot tell whether submitted images, audio, video or messages were AI-generated or fraudulent, and scattered tools give no traceable evidence.

Delivers

Reviewer-approved authenticity findings linked to case files

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 review time per case while keeping a traceable evidence record.

1. Detect whether images, audio, video or messages are AI-generated or fraudulent.
2. Scan submitted content in real time and return immediate provisional results.
3. Handle multiple content types in one case: images, audio, video and text.
4. Expose detection capabilities through an API for client systems.
5. Provide an interface for scanning and interpreting results without specialist training.
6. Keep detection algorithms current with new generative models and scam tactics.
7. Generate reports highlighting suspicious indicators and risk factors.
8. Send customizable alerts when potential threats match configured rules.
9. Operate without relying on personally identifiable information where possible.
10. Let users build and deploy detection models through a visual no-code interface.
11. Let users define targets, upload data and train custom models.
12. Provide a library of pre-trained models for common analysis tasks.
13. Support deployment to cloud, edge devices or existing workflows.
14. Detect AI-generated voices across languages and accents.
15. Remove background noise and music to improve audio detection accuracy.
16. Analyze short audio clips of less than seven seconds.
17. Offer a browser extension for quick checks on web content.
18. Verify identity documents to prevent fraudulent submissions.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewer-approved authenticity findings linked to case files with source references and unresolved questions.

Everything these tools do, in one app

• AI content detection Identifies whether images, audio, video, or messages are AI-generated or fraudulent.Found in AI or Not, Scam AI, Nuanced and 1 more
• Real-time analysis Scans and analyzes content in real time to provide immediate results.Found in Scam AI, EyePop.ai, AI Voice Detector
• Multi-format support Handles multiple content types such as images, audio, video, and text.Found in AI or Not, Scam AI, EyePop.ai and 1 more
• API integration Allows businesses to integrate detection capabilities into their own systems via API.Found in AI or Not, Nuanced
• User-friendly interface Provides an intuitive interface for easy scanning and interpretation of results.Found in AI or Not, Scam AI, EyePop.ai and 1 more
• Regular updates Keeps detection algorithms current with new AI models and scam tactics.Found in AI or Not, Scam AI
• Detailed reports Generates reports highlighting suspicious indicators and risk factors.Found in Scam AI
• Customizable alerts Notifies users of potential threats based on customizable settings.Found in Scam AI
• Privacy-first approach Operates without relying on personally identifiable information to protect user privacy.Found in Nuanced
• No-code platform Enables users to build and deploy AI models without coding through a visual interface.Found in EyePop.ai
• Custom model training Allows users to define targets, upload data, and train custom AI models.Found in EyePop.ai
• Pre-trained models Provides a library of ready-to-use models for common analysis tasks.Found in EyePop.ai
• Deployment flexibility Supports deployment to cloud, edge devices, or workflows for scalable use.Found in EyePop.ai
• Multilanguage support Detects AI-generated voices across various languages and accents.Found in AI Voice Detector
• Noise and music removal Removes background noise and music to improve audio detection accuracy.Found in AI Voice Detector
• Short audio detection Analyzes short audio clips (less than 7 seconds) effectively.Found in AI Voice Detector
• Browser extension Offers a browser extension for convenient access to detection tools.Found in AI Voice Detector
• KYC document verification Verifies identity documents to prevent fraudulent submissions.Found in AI or Not

What goes in, what comes out

The workflow

1. In
   Start with

   Submitted media, identity documents, message text and platform context

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect submitted media

4. 3

   Identity documents

5. 4

   Message text and platform context

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved authenticity findings linked to case files

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate detection results 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. Detection signals are probabilistic; final fraud and authenticity decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Case intake and evidence upload, Analysis workspace, Reviewer decision and report. Use a case queue with status filters, a large central viewer for image, audio, video and message evidence, and a right-hand panel for detector results, signals and comments. Let users compare original and processed media side by side. Display pending, escalated and approved states. Provide a client or partner link with findings anchored to the relevant evidence item. Make the task-specific outcome reviewer-approved authenticity findings linked to case files visible beside its evidence, review state and value baseline.

Accounts and administration

Case ownership, evidence versions, reviewer comments, approval states, usage allowances, retention 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

Client-owned case systems, authorized evidence sources and permitted platform data. Cloud storage, identity document systems and ticketing or case management 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: detect whether images, audio, video or messages are AI-generated or fraudulent; scan submitted content in real time and return immediate provisional results. 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 trust, safety and verification teams reviewing images, audio, video and messages use it to solve "reviewers cannot tell whether submitted images, audio, video or messages were AI-generated or fraudulent, and scattered tools give no traceable evidence"?
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: Reviewed cases per analyst hour and false-positive rate on held-out cases.
4. Measure, then decide. Track reviewed cases per analyst hour and false-positive rate on held-out cases; 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 content type family and one case queue; final fraud and authenticity decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: detect whether images, audio, video or messages are AI-generated or fraudulent; scan submitted content in real time and return immediate provisional results. Support the remaining modules with operator review: handle multiple content types in one case; generate reports highlighting suspicious indicators and risk factors. 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 authenticity findings linked to case files. Retain the explicit scope boundary: One content type family and one case queue; final fraud and authenticity decisions remain human.

What the build depends on. Evidence upload and preview, asynchronous detection jobs, editable version history, reviewer access and tested export formats. High-fidelity verification requires specialist fraud QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One content type family and one case queue; final fraud and authenticity decisions 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 case package. Offer a monthly review allowance after repeat demand. Quote complex video, edge deployment or specialist integration separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved authenticity findings linked to case files. 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 review time per case while keeping a traceable evidence record. Demonstrate a concrete reviewer-approved authenticity findings linked to case files using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Trust, safety and verification 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 authenticity findings linked to case files from a small authorized input set, with a transparent calculation of reviewed cases per analyst hour and false-positive rate on held-out cases and no promised savings.

The first 30 days

1. Week 1: interview five trust, safety and verification teams reviewing images, audio, video and messages and inspect a recent example of reviewers cannot tell whether submitted images, audio, video or messages were AI-generated or fraudulent, and scattered tools give no traceable evidence.
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 reviewed cases per analyst hour and false-positive rate on held-out cases, 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: Reviewed cases per analyst hour and false-positive rate on held-out cases. 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

Reviewed cases per analyst hour and false-positive rate on held-out cases; 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 authenticity findings linked to case files. 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 detection thresholds, case examples and reviewer corrections, together with reliable delivery for a narrow verification niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for trust, safety and verification teams reviewing images, audio, video and messages. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

AI or Not, Scam AI, Nuanced, EyePop.ai and AI Voice Detector, plus manual review and generic generation tools. Compare this product with the buyer's present method on reviewed cases per analyst hour and false-positive rate on held-out cases. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Detection attempts, media processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved authenticity findings linked to case files. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve evidence integrity, source attribution, chain of custody and usage permissions. Reviewers approve substantive findings and case scope. One content type family and one case queue; final fraud and authenticity decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.