Synthetic face library and rights console

For

Creative teams and studios producing people imagery for campaigns, products and media

Solves

Teams rent several face-generation tools, cannot search one owned library, and lack a clear rights record for each synthetic person image.

Delivers

Reviewed synthetic face assets with a rights record

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 tool subscriptions and rights ambiguity while keeping one searchable library of synthetic faces.

1. Generate realistic faces from simple inputs.
2. Adjust gender, age, ethnicity, clothing, hairstyle and skin tone.
3. Generate batches of faces for large projects.
4. Expose an API for developer integration.
5. Turn off invisible watermarks in privacy mode.
6. Provide FAQ and documentation in the console.
7. Search a pre-generated library of diverse faces.
8. Filter by tags or by uploading a similar face.
9. Support mobile-friendly access.
10. Offer a free account tier for personal projects.
11. Sign in with Google.
12. Download HD and standard resolutions.
13. Create portraits and headshots from uploaded photos.
14. Generate full-body images from keywords and prompts.
15. Mix up to four images into new facial compositions.
16. Create morph videos between facial images.
17. Export faces for 3D modeling projects.
18. Provide a no-code interface for non-programmers.
19. Record per-asset usage terms, model version and consent basis.
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 reviewed synthetic face asset with source references and unresolved questions.

Everything these tools do, in one app

• AI face generation Creates realistic human faces automatically from simple inputs.Found in Face Studio, Generated Photos, Lucidpic and 1 more
• Customizable facial parameters Lets users adjust attributes like gender, age, ethnicity, clothing, hairstyle, or skin tone to tailor the result.Found in Face Studio, Generated Photos, Lucidpic and 1 more
• Batch generation Produces multiple faces or images at once to save time on large projects.Found in Face Studio, Lucidpic
• API access Allows developers to integrate the face generation into their own software or workflows.Found in Face Studio, Generated Photos
• Privacy mode Turns off invisible watermarks on generated images for privacy or artistic needs.Found in Face Studio
• FAQ and documentation Provides help sections and references so users can learn the tool and troubleshoot.Found in Face Studio
• Pre-generated image library Offers a large searchable collection of ready-made diverse faces.Found in Generated Photos
• Advanced search and filtering Helps users quickly find images by tags or by uploading a similar face.Found in Generated Photos
• Mobile-friendly access Works well on mobile devices for on-the-go use.Found in Generated Photos
• Free account option Allows users to explore and download images for personal projects at no cost.Found in Generated Photos
• Google sign-in Lets users log in quickly using their Google account.Found in Lucidpic
• HD and standard downloads Provides images in different resolutions to suit various project needs.Found in Lucidpic
• Portrait and headshot creation Generates professional-looking headshots and avatars from uploaded photos or selfies.Found in Lucidpic
• Full-body image generation Creates detailed full-body images using keywords and prompts.Found in Generated Photos, Lucidpic
• Multi-image mixing Blends up to four images together to create unique facial compositions.Found in Face Mix
• Morph video creation Produces videos that show dynamic transitions between different facial images.Found in Face Mix
• 3D modeling support Generates faces that can be used in 3D modeling projects.Found in Face Mix
• No-code interface Makes the tool accessible to users without programming skills.Found in Face Mix

What goes in, what comes out

The workflow

1. In
   Start with

   Approved generation settings, attribute presets and usage rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved generation settings

4. 3

   Attribute presets and usage rules

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed synthetic face assets with a rights record

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. Synthetic faces must not depict real identifiable people without documented permission; final rights and likeness checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Library search and filters, Generation and parameter console, Asset detail and rights record, Batch job monitor, API and export settings. Use a thumbnail grid with tag and similarity filters, a large preview panel, and a right-hand panel for attributes, usage terms and review state. Let users compare candidate faces side by side. Display draft, approved and restricted states. Provide a client preview link with comments anchored to the asset. Make the task-specific outcome reviewed synthetic face assets with a rights record visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, generation 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 generation settings, approved attribute presets and permitted research sources. 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: generate realistic faces from simple inputs; adjust gender, age, ethnicity, clothing, hairstyle and skin tone. 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 creative teams and studios producing people imagery for campaigns, products and media use it to solve "teams rent several face-generation tools, cannot search one owned library, and lack a clear rights record for each synthetic person image"?
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: Accepted assets per production hour and rights queries resolved without escalation.
4. Measure, then decide. Track accepted assets per production hour and rights queries resolved without escalation; 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 attribute set and one output resolution; final rights and likeness checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate realistic faces from simple inputs; adjust gender, age, ethnicity, clothing, hairstyle and skin tone. Support the remaining modules with operator review: batch generation, library search, API access and rights record. 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 reviewed synthetic face assets with a rights record. Retain the explicit scope boundary: One approved attribute set and one output resolution; final rights and likeness checks remain human.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved attribute set and one output resolution; final rights and likeness 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 asset package. Offer a monthly production allowance after repeat demand. Quote complex video, 3D or specialist design separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed synthetic face asset with a rights record. 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 tool subscriptions and rights ambiguity while keeping one searchable library of synthetic faces. Demonstrate a concrete reviewed synthetic face asset with a rights record using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Creative teams and studios producing people imagery for campaigns, products and media professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample synthetic face asset with a rights record from a small authorized input set, with a transparent calculation of accepted assets per production hour and rights queries resolved without escalation and no promised savings.

The first 30 days

1. Week 1: interview five creative teams and studios producing people imagery for campaigns, products and media and inspect a recent example of rented face-generation tools, no single owned library and unclear rights records.
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 accepted assets per production hour and rights queries resolved without escalation, 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: Accepted assets per production hour and rights queries resolved without escalation. 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

Accepted assets per production hour and rights queries resolved without escalation; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed synthetic face assets with a rights record. 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 attributes, usage rules and review examples, together with reliable delivery for a narrow creative niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for creative teams and studios producing people imagery for campaigns, products and media. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Face Studio, Generated Photos, Lucidpic and Face Mix, plus freelancers and stock photo libraries. Compare this product with the buyer's present method on accepted assets per production hour and rights queries resolved without escalation. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, image and video 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 reviewed synthetic face assets with a rights record. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve likeness rights, source attribution, usage permissions and consent records. Named owners approve substantive changes and publication scope. One approved attribute set and one output resolution; final rights and likeness checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.