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AI app for it and development · no coding needed

Face identity verification delivery workspace

Reduce the number of rented face services and keep identity data and review rules inside one owned workspace.

Made for: Product and platform teams adding face-based identity checks to their own applications

What Face identity verification delivery workspace looks like
Open the demo For members · a working demo with sample data

What it does for you

The problem

Face verification is split across several rented recognition, liveness and deepfake services, so identity data, review rules and integration work sit outside the buyer's control.

What it gives you

Reviewer-approved identity verification decisions linked to an audit record

What you give it

Permitted face imagesvideo framesidentity recordsaccess rules

Build your own version of Facia, Luxand.Cloud and more

One app with what these 3 AI tools do, yours to keep and change: Facia, Luxand.Cloud, InstantID.

Everything these tools do, in one app

  • Facial recognition Recognizes and compares human faces to identify individuals.Found in Facia, Luxand.Cloud, InstantID
  • Face matching Matches a face against another face or a database to verify identity.Found in Facia, Luxand.Cloud
  • Liveness detection Confirms a live person is present to prevent spoofing.Found in Facia
  • Deepfake detection Identifies AI-generated images and deepfake videos to combat identity fraud.Found in Facia
  • Facial attribute detection Detects age, gender, and emotions from a face.Found in Luxand.Cloud
  • Instant identification Provides quick and accurate user identification in real time.Found in InstantID
  • Secure data storage Protects sensitive biometric data, such as by not storing actual photos.Found in Facia, Luxand.Cloud
  • API integration Allows developers to integrate facial recognition into applications via APIs.Found in Facia, Luxand.Cloud
  • SDK support Provides software development kits for iOS and Android.Found in Facia
  • Multi-language support Compatible with multiple programming languages for development.Found in Luxand.Cloud
  • Scalability Handles applications from small to large enterprise scale.Found in Luxand.Cloud
  • Customization Offers customizable solutions to meet specific needs.Found in Facia
  • No hardware requirement Works without specific hardware requirements.Found in Facia
  • Low-light operation Functions effectively in low-light conditions.Found in Facia
  • Accessory tolerance Works with accessories like hats and glasses.Found in Facia
  • Bias minimization Reduces demographic and racial biases in recognition.Found in Facia
  • Streamlined processes Automates credential validation and login processes.Found in InstantID
  • System integration Integrates with existing systems for smooth user experience.Found in InstantID

How it works, step by step

  1. Recognize and compare faces across permitted images and video frames
  2. Match a face against another face or an enrolled record
  3. Confirm a live person is present to prevent spoofing
  4. Flag AI-generated images and deepfake video for review
  5. Detect age, gender and emotion attributes where permitted
  6. Return quick identification results in real time
  7. Store biometric templates instead of raw photos
  8. Expose recognition and matching through APIs
  9. Provide iOS and Android SDKs
  10. Support multiple programming languages
  11. Scale from small pilots to enterprise volume
  12. Allow custom thresholds, rules and review steps
  13. Run without dedicated hardware
  14. Operate in low-light conditions
  15. Tolerate hats, glasses and similar accessories
  16. Measure and reduce demographic bias on held-out cases
  17. Automate credential validation and login steps
  18. Integrate with existing identity and access systems
  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 identity verification decisions linked to an audit record with source references and unresolved questions

Build it yourself with your AI system

Build this app yourself, no coding needed

Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.

Sign in to see how to build it yourself

Build a quick version to try, or get the full app pack for Face identity verification delivery workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.

Sign in Become a member

4 Have it built for you days to a few weeks

Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Face identity verification delivery workspace with you.

Have Nexibeo build it

What's in the app pack

Included in the Complete AI Training membership.

  • The building instructions your AI follows, step by step
  • The questions your AI will ask you about your business before it starts
  • A clickable demo you can open in your browser, to see how it should work
  • A detailed blueprint of the screens, the information it keeps and the checks it runs

Become a member to get the app packAlready a member? Sign in

The files, for the technically curious
  • START-HERE.mdHow to build it with your own AI (read first)3 KB
  • README.mdOverview and links3 KB
  • questions.mdQuestions to answer before you build2 KB
  • prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
  • prompt-vps.mdThe same build on your own server (Docker)26 KB
  • spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
  • demo/index.htmlThe working demo on sample data201 KB

Questions

Do I need to know how to code?

No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.

What does it cost?

The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.

How long does it take?

The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.

Can I change it to fit my business?

Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.

More detailsHow the AI works, safeguards and what to build first

Reduce the number of rented face services and keep identity data and review rules inside one owned workspace. For product and platform teams adding face-based identity checks to their own applications, convert permitted face images, video frames, identity records and access rules into reviewer-approved identity verification decisions linked to an audit record. The benefit is a testable hypothesis, measured through accepted verifications per review hour and false accept and false reject rates on held-out cases; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect permitted face images, video frames, identity records and access rules, then follow this sequence: 1. Recognize and compare faces across permitted images and video frames. 2. Match a face against another face or an enrolled record. 3. Confirm a live person is present to prevent spoofing. 4. Flag AI-generated images and deepfake video for review. 5. Detect age, gender and emotion attributes where permitted. 6. Return quick identification results in real time. Resolve uncertain cases with qualified reviewers, approve reviewer-approved identity verification decisions linked to an audit record, and measure accepted verifications per review hour and false accept and false reject rates on held-out cases against a documented baseline.

How the AI works

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. One fixed consent model and permitted identity dataset; final identity decisions and bias checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve consent, source attribution, identity accuracy and usage permissions. Identity owners approve substantive changes and verification scope. One fixed consent model and permitted identity dataset; final identity decisions and bias checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One fixed consent model and permitted identity dataset; final identity decisions and bias checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: recognize and compare faces across permitted images and video frames; match a face against another face or an enrolled record. Support the third module with operator review: confirm a live person is present to prevent spoofing. 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.

What it can connect to

Buyer-owned identity records, authorized face datasets and permitted access systems. Cloud asset storage, identity-provider import/export and access destinations. Start with file exchange and validate destination specifications before promising direct provisioning. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Verification setup and consent, Editable decision review, Client audit and delivery. Use a thumbnail gallery for verification sessions, a large central review canvas, and a right-hand panel for evidence, thresholds and comments. Let users compare captured frames side by side. Display pending, changes requested and approved states. Provide a client audit link with comments anchored to the relevant session. Make the task-specific outcome reviewer-approved identity verification decisions linked to an audit record visible beside its evidence, review state and value baseline.