Managed AI image production and review platform

For

Creative teams and studios producing AI-generated images for campaigns and products

Solves

Image generation, editing, review and publishing are spread across several rented tools, so prompts, versions and approvals are hard to trace and the team does not own its workflow.

Delivers

Approved image sets linked to campaign or product use

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

Consolidate generation, editing, review and publishing into one owned platform.

1. Generate original images from text descriptions.
2. Transform existing images from text or reference input.
3. Produce multiple variations of an image.
4. Inpaint and outpaint to add or remove elements.
5. Upscale images for resolution and sharpness.
6. Refine faces in generated images.
7. Select or train custom models for styles or objects.
8. Adjust generation parameters.
9. Organize, store and retrieve generated images.
10. Run generation locally where privacy or speed requires it.
11. Generate batches from different prompts.
12. Apply negative prompts to exclude unwanted content.
13. Provide keyboard shortcuts for repeated actions.
14. Attach metadata to generated images.
15. Browse community artwork for reference.
16. Offer specialized generators such as anime and avatar tools.
17. Control pose precisely.
18. Schedule and auto-share approved content to social channels.
19. Accept text commands to adjust generation settings.
20. Apply LoRa models for detailed style control.
21. Support multi-user editing and review.
22. Save and revisit prompts and resulting art.
23. Compare the reviewed result with the recorded baseline and value assumptions.
24. Capture corrections and named-owner approval before consequential use.
25. Export a versioned approved image set linked to campaign or product use with source references and unresolved questions.

Everything these tools do, in one app

• Text-to-image generation Creates original images from text descriptions.Found in dreamlike.art, Invoke AI, DiffusionBee and 4 more
• Image-to-image transformation Modifies existing images based on text input or other images.Found in dreamlike.art, Invoke AI, DiffusionBee
• Image variations Generates multiple variations of an image to explore creative directions.Found in dreamlike.art
• Inpainting and outpainting Edits or extends images by adding or removing elements intelligently.Found in Invoke AI, DiffusionBee, ARTSMART AI
• Upscaling Improves image resolution and sharpness using AI.Found in dreamlike.art, DiffusionBee, ARTSMART AI
• Face refinement Enhances and refines faces in generated images.Found in dreamlike.art
• Custom models Allows users to select or train custom AI models for specific styles or objects.Found in dreamlike.art, DiffusionBee
• Adjustable parameters Provides settings to fine-tune image generation outputs.Found in dreamlike.art, Invoke AI, Distillery
• Image management Organizes, stores, and retrieves generated images.Found in dreamlike.art, Playbook AI
• Local processing Runs image generation locally on the user's device for privacy and speed.Found in DiffusionBee, Amazing AI
• Batch generation Generates multiple images simultaneously from different prompts.Found in Amazing AI
• Negative prompts Refines results by specifying what to exclude from the image.Found in Amazing AI, Distillery
• Keyboard shortcuts Offers shortcuts to streamline the workflow.Found in Amazing AI
• Metadata support Includes metadata with generated images for organization.Found in Amazing AI
• Community artwork browser Browses and draws inspiration from community-generated artworks.Found in ARTSMART AI
• Specialized generators Includes niche generators like anime creator and avatar generator.Found in ARTSMART AI
• Pose control Manipulates image poses precisely.Found in ARTSMART AI
• Social media planner Schedules and auto-shares content on social media.Found in ARTSMART AI
• Command-based interface Uses text commands to adjust generation settings.Found in Distillery
• LoRa models Provides a collection of LoRa models for detailed style control.Found in Distillery
• Collaboration tools Enables multiple users to edit and review AI-generated art.Found in Playbook AI
• Prompt storage Saves and revisits AI prompts and resulting art.Found in Playbook AI

What goes in, what comes out

The workflow

1. In
   Start with

   Text prompts, reference images, brand rules and reviewer notes

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect text prompts

4. 3

   Reference images

5. 4

   Brand rules and reviewer notes

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Approved image sets linked to campaign or product use

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 fixed brand ruleset and licensed model set; final rights, likeness and brand checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Prompt and model workspace, Editable image canvas, Review and delivery board. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for prompts, model settings, references 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 image. Make the task-specific outcome approved image sets linked to campaign or product use 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 asset libraries, authorized reference images 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

   7 days

   One buyer segment, one recurring use case; first modules: generate original images from text descriptions; transform existing images from text or reference input. 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 AI-generated images for campaigns and products use it to solve "image generation, editing, review and publishing are spread across several rented tools, so prompts, versions and approvals are hard to trace and the team does not own its workflow"?
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 images per production hour and corrections after approval.
4. Measure, then decide. Track approved images per production hour and corrections after 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 fixed brand ruleset and licensed model set; final rights, likeness and brand checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate original images from text descriptions; transform existing images from text or reference input. Support the remaining modules with operator review: produce multiple variations of an image; inpaint and outpaint to add or remove elements; upscale images for resolution and sharpness; refine faces in generated images. 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 approved image sets linked to campaign or product use. Retain the explicit scope boundary: One fixed brand ruleset and licensed model set; final rights, likeness and brand 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 fixed brand ruleset and licensed model set; final rights, likeness and brand 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 image 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 approved image set linked to campaign or product use. 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

Consolidate generation, editing, review and publishing into one owned platform. Demonstrate a concrete approved image set linked to campaign or product use using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Creative teams and studios producing AI-generated images for campaigns and products professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample approved image set linked to campaign or product use from a small authorized input set, with a transparent calculation of approved images per production hour and corrections after approval and no promised savings.

The first 30 days

1. Week 1: interview five creative teams and studios producing AI-generated images for campaigns and products and inspect a recent example of image generation, editing, review and publishing spread across several rented tools.
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 approved images per production hour and corrections after 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 images per production hour and corrections after 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 images per production hour and corrections after approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs approved image sets linked to campaign or product use. 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, brand 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 AI-generated images for campaigns and products. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

dreamlike.art, Invoke AI, DiffusionBee, Amazing AI, ARTSMART AI, Distillery and Playbook AI. Compare this product with the buyer's present method on approved images per production hour and corrections after 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, 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 approved image sets linked to campaign or product use. Track cost per accepted output, including correction work, unsuccessful cases and support.

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