Managed AI visual production workbench

For

Creative teams and studios producing image, audio and video assets for clients

Solves

Creative teams rent several separate AI tools for image, audio and video work, so assets, versions and approvals are scattered across subscriptions they do not own.

Delivers

Reviewed, client-approved image, audio and video deliverables linked to source references

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce tool sprawl and review cycles while keeping one owned asset pipeline.

1. Generate images from text descriptions.
2. Transform or enhance existing images from instructions.
3. Remove backgrounds from images or videos.
4. Remove unwanted objects or text from images or videos.
5. Upscale image resolution and quality.
6. Upscale video resolution and quality.
7. Restore old or damaged images.
8. Restore old or damaged videos.
9. Add color to black and white photos or videos.
10. Generate videos from text prompts.
11. Convert audio into video content.
12. Generate music or sound effects from text.
13. Transform voice characteristics.
14. Transcribe spoken content into written text.
15. Generate subtitles from audio.
16. Swap faces in photos or videos.
17. Synchronize lip movements with audio.
18. Apply artistic styles to images.
19. Train custom models such as DreamBooth or LoRA.
20. Expand images beyond their original borders.
21. Animate still images into live photos, wallpapers or GIFs.
22. Modify facial features, expressions, age or gender.
23. Virtually try on clothes.
24. Work together on projects in a shared space.
25. Integrate tools into other workflows via API.
26. Compare the reviewed result with the recorded baseline and value assumptions.
27. Capture corrections and named-owner approval before consequential use.
28. Export a versioned reviewed, client-approved image, audio and video deliverables linked to source references with source references and unresolved questions.

Everything these tools do, in one app

• Text to Image Generate images from text descriptions.Found in Artificial Studio, Dzine AI, IMGCreator.ai and 2 more
• Image to Image Transform or enhance existing images based on instructions.Found in Dzine AI, IMGCreator.ai, Neural Love
• Background Removal Remove backgrounds from images or videos.Found in Artificial Studio, Dzine AI, AILab Tools and 1 more
• Object Removal Remove unwanted objects or text from images or videos.Found in Dzine AI, AILab Tools, Wunjo
• Image Upscaling Enhance image resolution and quality.Found in Dzine AI, Neural Love, AILab Tools
• Video Upscaling Enhance video resolution and quality.Found in Neural Love
• Image Restoration Restore old or damaged images.Found in Artificial Studio, Neural Love
• Video Restoration Restore old or damaged videos.Found in Neural Love
• Colorization Add color to black and white photos or videos.Found in Artificial Studio, Neural Love, AILab Tools
• Text to Video Generate videos from text prompts.Found in Artificial Studio
• Audio to Video Convert audio into video content.Found in Artificial Studio
• Text to Audio Generate music or sound effects from text.Found in Artificial Studio
• Voice Transfer Transform voice characteristics.Found in Artificial Studio
• Audio to Text Transcribe spoken content into written text.Found in Artificial Studio
• Audio to Subtitles Generate subtitles from audio.Found in Artificial Studio
• Face Swap Swap faces in photos or videos.Found in Wunjo
• Lip Sync Synchronize lip movements with audio.Found in Wunjo
• Style Transfer Apply artistic styles to images.Found in Dzine AI, IMGCreator.ai
• Model Training Train custom AI models like DreamBooth or LoRA.Found in Neural Love, Phygital+
• Outpainting Expand images beyond their original borders.Found in Artificial Studio, Neural Love
• Photo Animation Animate still images into live photos, wallpapers, or GIFs.Found in PixaMotion
• Face Editing Modify facial features, expressions, age, or gender.Found in AILab Tools
• Virtual Try-On Virtually try on clothes.Found in Dzine AI, AILab Tools
• Collaborative Workspace Work together on projects in a shared space.Found in Phygital+
• API Access Integrate tools into other workflows via API.Found in AILab Tools, Wunjo

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed source assets, briefs, brand rules and usage constraints

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed source assets

4. 3

   Briefs

5. 4

   Brand rules and usage constraints

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, client-approved image, audio and video deliverables linked to source references

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. Final brand, likeness, rights and publication checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Creative brief and references, Editable production preview, Client proof and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for references, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant asset. Make the task-specific outcome reviewed, client-approved image, audio and video deliverables linked to source references 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

Client-owned asset libraries, authorized brand kits and permitted stock 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

   7 days

   One buyer segment, one recurring use case; first modules: generate images from text descriptions; transform or enhance existing images from instructions. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 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 image, audio and video assets for clients use it to solve "creative teams rent several separate AI tools for image, audio and video work, so assets, versions and approvals are scattered across subscriptions they do not own"?
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 corrections after client approval.
4. Measure, then decide. Track accepted assets per production hour and corrections after client approval; 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: generate images from text descriptions; transform or enhance existing images from instructions. Support the remaining modules with operator review. 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, client-approved image, audio and video deliverables linked to source references. Retain the explicit scope boundary: final brand, likeness, rights and publication 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: final brand, likeness, rights and publication 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, client-approved image, audio and video deliverables linked to source references. 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 sprawl and review cycles while keeping one owned asset pipeline. Demonstrate a concrete reviewed, client-approved image, audio and video deliverables linked to source references using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Creative teams and studios producing image, audio and video assets for clients professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, client-approved image, audio and video deliverables linked to source references from a small authorized input set, with a transparent calculation of accepted assets per production hour and corrections after client approval and no promised savings.

The first 30 days

1. Week 1: interview five creative teams and studios producing image, audio and video assets for clients and inspect a recent example of scattered assets, versions and approvals across separate AI subscriptions.
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 corrections after client approval, 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 corrections after client approval. 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 corrections after client approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, client-approved image, audio and video deliverables linked to source references. 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 styles, production constraints 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 image, audio and video assets for clients. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Artificial Studio, Dzine AI, IMGCreator.ai, Neural Love, Phygital+, PixaMotion, AILab Tools and Wunjo, plus freelancers, creative agencies and generic generation tools. Compare this product with the buyer's present method on accepted assets per production hour and corrections after client approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, video or image 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, client-approved image, audio and video deliverables linked to source references. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve brand voice, source attribution, likeness permissions and usage rights. Named owners approve substantive changes and publication scope. Final brand, likeness, rights and publication checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.