Live and file media authenticity verification workspace

For

Security, trust and safety, and IT teams verifying live calls, uploaded media and online content

Solves

Synthetic video, cloned voices and manipulated files pass review in live calls and shared content, and reviewers cannot show what was checked or why a verdict was reached.

Delivers

Reviewer-confirmed authenticity verdicts linked to evidence

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

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

Reduce exposure to undetected synthetic media while keeping a reviewable record of each verdict.

1. Check whether the person on screen is genuine during live video calls.
2. Run alongside Zoom, Google Meet, Teams and Discord.
3. Operate without recording calls, accessing audio or storing user data.
4. Process on-device without sending data to the cloud.
5. Verify authenticity with a single click.
6. Detect synthetic video and static photo spoofing.
7. Support several deepfake generators with ongoing additions.
8. Filter bad lighting, shaky video, heavy compression and distant faces to reduce false positives.
9. Read the meeting window directly via Windows Graphics Capture without virtual camera setup.
10. Fine-tune the detection model against new deepfake methods through an automated data collection pipeline.
11. Analyze both audio and video files for authenticity.
12. Route uncertain cases to expert analysis for a probability assessment.
13. Remove background noise and music for cleaner media analysis.
14. Detect on YouTube, WhatsApp, TikTok, Zoom and Google Meet through a browser extension.
15. Accept uploaded content for immediate evaluation and detailed insights.
16. Accept forwarded images, video or voice notes on WhatsApp and return a verdict within seconds.
17. Run detection in the background without manual uploads or a separate dashboard.
18. Let users run the tool's detection, a partner detection module, or both.
19. Detect cloned voices during live phone calls.
20. Compare the reviewed result with the recorded baseline and value assumptions.
21. Capture corrections and named-owner approval before consequential use.
22. Export a versioned reviewer-confirmed authenticity verdicts linked to evidence with source references and unresolved questions.

Everything these tools do, in one app

• Real-time video call detection Checks whether the person on screen is genuine during live video calls.Found in Fakeradar, Halo by Scam AI
• Platform integration Works alongside common video conferencing platforms like Zoom, Google Meet, Teams, and Discord.Found in Fakeradar, Halo by Scam AI, Deepfake Detector
• Privacy-first operation Does not record calls, access audio, or store user data.Found in Fakeradar, Halo by Scam AI
• On-device processing Runs locally without sending data to the cloud.Found in Halo by Scam AI
• One-click verification Allows users to verify authenticity with a single click.Found in Fakeradar
• Detection of deepfakes and static photos Identifies synthetic video and static photo spoofing attempts.Found in Fakeradar
• Multiple deepfake generator support Detects content from several deepfake generators, with ongoing additions planned.Found in Fakeradar
• Quality check filters Filters out bad lighting, shaky video, heavy compression, or faces too far from the camera to reduce false positives.Found in Halo by Scam AI
• Windows Graphics Capture Reads the meeting window directly, working across desktop apps and browser-based calls without virtual camera setup.Found in Halo by Scam AI
• Automated model fine-tuning Fine-tunes the detection model against new deepfake methods via an automated data collection pipeline.Found in Halo by Scam AI
• Audio and video analysis Scrutinizes both audio and video files for authenticity.Found in Deepfake Detector
• Expert verification services Uses expert analysis to determine the probability of media being AI-generated or authentic.Found in Deepfake Detector
• Noise and music remover Removes background noise and music to provide cleaner media analysis.Found in Deepfake Detector
• Browser extension Enables detection on popular platforms like YouTube, WhatsApp, TikTok, Zoom, and Google Meet.Found in Deepfake Detector, deepeye by deepidv
• Content upload for evaluation Allows users to upload content for immediate evaluation and detailed insights.Found in Deepfake Detector
• WhatsApp integration Accepts forwarded images, video, or voice notes and returns a verdict within seconds.Found in deepeye by deepidv
• No file uploads required Runs detection in the background without requiring manual uploads or a separate dashboard.Found in deepeye by deepidv
• Optional premium module Lets users run the tool's detection, Scam.AI's detection, or both.Found in deepeye by deepidv
• Cloned voice detection Detects cloned voices during live phone calls.Found in deepeye by deepidv

What goes in, what comes out

The workflow

1. In
   Start with

   Live call frames, uploaded video and audio files, forwarded images and voice notes, and platform content

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect live call frames

4. 3

   Uploaded video and audio files

5. 4

   Forwarded images and voice notes

6. 5

   Platform content

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewer-confirmed authenticity verdicts linked to evidence

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

What your team sees

Primary screens: Detection setup and sources, Live call and file review, Verdict record and delivery. Use a thumbnail gallery for cases, a large central review canvas with the media and signal timeline, and a right-hand panel for quality flags, model signals and reviewer comments. Let users compare signals side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant frame or segment. Make the task-specific outcome reviewer-confirmed authenticity verdicts linked to evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage 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

Zoom, Google Meet, Teams, Discord, YouTube, WhatsApp and TikTok via browser extension and Windows Graphics Capture. Cloud asset storage, design-file import/export 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: check whether the person on screen is genuine during live video calls; run alongside Zoom, Google Meet, Teams and Discord. 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

   3 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 security, trust and safety, and IT teams verifying live calls, uploaded media and online content use it to solve "synthetic video, cloned voices and manipulated files pass review in live calls and shared content, and reviewers cannot show what was checked or why a verdict was reached"?
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: Confirmed verdicts per review hour and false positives after reviewer correction.
4. Measure, then decide. Track confirmed verdicts per review hour and false positives after reviewer 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: One approved input format, a bounded representative case set and the first two task modules: check whether the person on screen is genuine during live video calls; run alongside Zoom, Google Meet, Teams and Discord. Support the remaining modules with operator review: operate without recording calls, accessing audio or storing user data; process on-device without sending data to the cloud; verify authenticity with a single click; detect synthetic video and static photo spoofing; support several deepfake generators with ongoing additions; filter bad lighting, shaky video, heavy compression and distant faces to reduce false positives; read the meeting window directly via Windows Graphics Capture without virtual camera setup; fine-tune the detection model against new deepfake methods through an automated data collection pipeline; analyze both audio and video files for authenticity; route uncertain cases to expert analysis for a probability assessment; remove background noise and music for cleaner media analysis; detect on YouTube, WhatsApp, TikTok, Zoom and Google Meet through a browser extension; accept uploaded content for immediate evaluation and detailed insights; accept forwarded images, video or voice notes on WhatsApp and return a verdict within seconds; run detection in the background without manual uploads or a separate dashboard; let users run the tool's detection, a partner detection module, or both; detect cloned voices during live phone calls. 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-confirmed authenticity verdicts linked to evidence. Retain the explicit scope boundary: detection signals are probabilistic; final authenticity and fraud judgments remain human.

What the build depends on. Asset upload and preview, asynchronous detection jobs, editable version history, reviewer access and tested export formats. High-fidelity detection requires specialist security QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: detection signals are probabilistic; final authenticity and fraud judgments 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 detection package. Offer a monthly review allowance after repeat demand. Quote complex live-call or multi-platform coverage separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-confirmed authenticity verdicts linked to evidence. 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 exposure to undetected synthetic media while keeping a reviewable record of each verdict. Demonstrate a concrete reviewer-confirmed authenticity verdicts linked to evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Security, trust and safety, and IT teams verifying live calls, uploaded media and online content professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-confirmed authenticity verdicts linked to evidence from a small authorized input set, with a transparent calculation of confirmed verdicts per review hour and false positives after reviewer correction and no promised savings.

The first 30 days

1. Week 1: interview five security, trust and safety, and IT teams verifying live calls, uploaded media and online content and inspect a recent example of synthetic video, cloned voices and manipulated files pass review in live calls and shared content.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure confirmed verdicts per review hour and false positives after reviewer 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: Confirmed verdicts per review hour and false positives after reviewer 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

Confirmed verdicts per review hour and false positives after reviewer correction; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-confirmed authenticity verdicts linked to evidence. 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 configurations, generator coverage and review examples, together with reliable delivery for a narrow security niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for security, trust and safety, and IT teams verifying live calls, uploaded media and online content. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Fakeradar, Halo by Scam AI, Deepfake Detector and deepeye by deepidv. Compare this product with the buyer's present method on confirmed verdicts per review hour and false positives after reviewer correction. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Detection attempts, video and audio 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-confirmed authenticity verdicts linked to evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, chain of custody and usage permissions. Reviewers approve substantive verdicts and disclosure scope. Detection signals are probabilistic; final authenticity and fraud judgments remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.