Managed AI visual production workbench

For

Creative teams and studios producing campaign and product visuals

Solves

Visual production is split across several rented AI tools, so assets, styles and approvals live in different places.

Delivers

Reviewer-approved visual assets linked to campaign briefs

Built in

about 6 weeks of creation time, MVP in 7 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 sprawl and review cycles while keeping one approved visual library.

1. Generate images from text or image inputs.
2. Generate video from prompts or image inputs.
3. Produce multiple variations of an existing image.
4. Remove image backgrounds.
5. Upscale images to higher resolution.
6. Inpaint missing areas and outpaint beyond image edges.
7. Extract, reposition or remove objects.
8. Apply prebuilt style presets.
9. Edit with natural language text or voice commands.
10. Train a custom model on approved brand images.
11. Route tasks across multiple AI models.
12. Render results on a live iterative canvas.
13. Generate scalable vector illustrations.
14. Browse and download from a pre-generated image library.
15. Access templates and reusable assets.
16. Process and enhance audio for media output.
17. Work inside Figma on design assets.
18. Edit with brush tools to add, subtract or color details.
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 visual assets linked to campaign briefs with source references and unresolved questions.

Everything these tools do, in one app

• AI image generation Create new images from text prompts or other inputs using AI.Found in KREA, Kittl, Impossible Images and 5 more
• AI video generation Generate videos using AI from prompts or other inputs.Found in KREA
• Image variation generation Produce multiple distinct variations of an existing image.Found in KREA, Kittl, Stable Diffusion Reimagine
• Background removal Remove the background from an image to isolate the subject.Found in Kittl, Phedra AI, Hippo AI
• Image upscaling Increase image resolution to improve quality and clarity.Found in KREA, Phedra AI, Hippo AI
• Inpainting and outpainting Fill in missing parts of an image or extend it beyond its original boundaries.Found in KREA, Hippo AI
• Object manipulation Precisely extract, reposition, or remove objects within an image.Found in KREA, Phedra AI, Hippo AI
• Style presets Apply prebuilt artistic styles to generated or edited images.Found in Kittl, Phedra AI, Hippo AI
• Text and voice editing Modify images using natural language text or voice commands.Found in Phedra AI
• Custom model training Train an AI model on your own images to generate personalized outputs.Found in KREA, Astria
• Multi-model support Access and use multiple AI models for different styles and outcomes.Found in KREA, ExtendImageAI, Hippo AI
• Real-time rendering See instant feedback and iterative results on a live canvas.Found in KREA, Fal.ai
• Vector illustration generation Create scalable vector illustrations suitable for web and mobile apps.Found in Hippo AI
• Image library browsing Browse and download from a collection of pre-generated images.Found in Impossible Images
• Template and asset library Access pre-designed templates and assets for quick visual production.Found in Phedra AI
• Audio processing Process and enhance audio as part of media creation.Found in Fal.ai
• Figma integration Work directly within Figma to generate and edit design assets.Found in Hippo AI
• Brush-based editing Use intuitive brush tools to add, subtract, or color details precisely.Found in MagicQuill

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed brand assets, approved references, model choices and review constraints

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed brand assets

4. 3

   Approved references

5. 4

   Model choices and review constraints

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved visual assets linked to campaign briefs

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. One approved brand asset set and licensed model list; final brand and rights checks remain editorial. 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 reviewer-approved visual assets linked to campaign briefs 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

Brand-owned asset libraries, authorized references and permitted model providers. 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 or image inputs; generate video from prompts or image inputs. 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 campaign and product visuals use it to solve "visual production is split across several rented AI tools, so assets, styles and approvals live in different places"?
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: Approved visual assets per production hour and corrections after creative approval.
4. Measure, then decide. Track approved visual assets per production hour and corrections after creative 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 brand asset set and licensed model list; final brand and rights checks remain editorial. Implement one approved input format, a bounded representative case set and the first two task modules: generate images from text or image inputs; generate video from prompts or image inputs. Support the remaining modules with operator review: produce variations, remove backgrounds, upscale, inpaint and outpaint, manipulate objects, apply styles, edit by text or voice, train custom models, route across models, render live, generate vectors, browse libraries, use templates, process audio, work in Figma and brush-edit. 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 visual assets linked to campaign briefs. Retain the explicit scope boundary: One approved brand asset set and licensed model list; final brand and rights checks remain editorial.

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 brand asset set and licensed model list; final brand and rights checks remain editorial.

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 reviewer-approved visual assets linked to campaign briefs. 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 approved visual library. Demonstrate a concrete reviewer-approved visual assets linked to campaign briefs using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Creative teams and studios producing campaign and product visuals 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 visual assets linked to campaign briefs from a small authorized input set, with a transparent calculation of approved visual assets per production hour and corrections after creative approval and no promised savings.

The first 30 days

1. Week 1: interview five creative teams and studios producing campaign and product visuals and inspect a recent example of visual production split across several rented AI tools, so assets, styles and approvals live in different places.
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 approved visual assets per production hour and corrections after creative 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: Approved visual assets per production hour and corrections after creative 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

Approved visual assets per production hour and corrections after creative approval; 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 visual assets linked to campaign briefs. 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 campaign and product visuals. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

KREA, Kittl, Impossible Images, Phedra AI, Fal.ai, ExtendImageAI, Hippo AI, MagicQuill, Astria and Stable Diffusion Reimagine. Compare this product with the buyer's present method on approved visual assets per production hour and corrections after creative 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 reviewer-approved visual assets linked to campaign briefs. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve brand voice, source attribution, asset accuracy and usage permissions. Brand owners approve substantive changes and publication scope. One approved brand asset set and licensed model list; final brand and rights checks remain editorial. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.