
Managed visual canvas production workbench
Reduce tool switching and style drift while keeping one reviewed visual pipeline.
- For
- Creative teams and studios producing visual artwork for print and social channels
- Solves
- Visual work is split across a canvas tool, a style tool and a generation tool, so files, styles and approvals drift apart.
- Delivers
- Reviewer-approved visual assets linked to export-ready frames
- Built in
- about 5 weeks of creation time, MVP in 6 days
- Investment
- $13,000 for the MVP, $44,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
Reduce tool switching and style drift while keeping one reviewed visual pipeline.
- Arrange elements on an infinite canvas workspace.
- Manage layouts with layer-based composition.
- Compose scenes within frames and export selected areas.
- Place and manipulate elements by drag and drop.
- Convert rough sketches into refined generated art.
- Let AI create and arrange files, notes and images on the board.
- Translate design ideas into prompts through interactive dialogue.
- Apply consistent styles across elements while preserving composition.
- Offer a built-in library of visual styles.
- Import reference images to create variations or standalone assets.
- Insert or remove image parts without affecting the rest of the composition.
- Export finished images at high resolution for print and social use.
- Read the whole workspace and the relative positions of elements.
- Organize scattered ideas, suggest directions and draft a roadmap from board content.
- Create functional widgets such as charts or interactive elements on the canvas.
- Hand off project plans to external agents while preserving spatial context.
- Charge one credit per image generation, with failed generations not consuming credits.
- Compare the reviewed result with the recorded baseline and value assumptions.
- Capture corrections and named-owner approval before consequential use.
- Export a versioned reviewer-approved visual assets linked to export-ready frames with source references and unresolved questions.
Everything these tools do, in one app
- Infinite canvas workspace Provides a boundless surface for arranging and composing visual elements.Found in CanvAi, Causal
- Layer-based composition Lets you manage layouts precisely using canvases and layers.Found in Stylar
- Frame-based workflow Compose scenes within frames and export specific areas.Found in CanvAi
- Drag-and-drop interface Enables intuitive placement and manipulation of elements on the canvas.Found in Stylar
- Sketch-to-image generation Converts rough sketches into refined AI-generated art.Found in CanvAi
- Generative canvas AI actively creates and arranges files, notes, and images directly on the board.Found in Causal
- AI-assisted prompt generation Translates design ideas into effective AI prompts through interactive dialogue.Found in Stylar
- Coherent stylization Applies consistent styles automatically across elements while preserving composition.Found in Stylar
- Style library Offers a wide range of built-in visual styles to choose from.Found in Stylar
- Reference image import Allows importing reference images to create variations or standalone assets.Found in CanvAi
- Image editing tools Enables inserting or removing image parts without affecting the rest of the composition.Found in Stylar
- High-resolution export Exports finished images at high resolution suitable for print and social use.Found in CanvAi
- Multimodal spatial context AI understands the entire workspace and the relative positions of elements.Found in Causal
- AI organization and brainstorming Organizes scattered ideas, suggests new directions, or generates a roadmap from board content.Found in Causal
- Custom widget creation Creates functional elements like habit trackers, bar charts, or interactive games on the canvas.Found in Causal
- MCP connections Hands off project plans to external agents, preserving spatial context.Found in Causal
- Credit-based generation Charges 1 credit per image generation, with failed generations not consuming credits.Found in CanvAi
What goes in, what comes out
- Licensed references
- Sketches
- Brand style guides
- Layout constraints
AI drafts, people review. Visual production platform with managed creative review.
- Reviewer-approved visual assets linked to export-ready frames
How it works
The workflow
- InStart with
Licensed references, sketches, brand style guides and layout constraints
- 1
Confirm the buyer's problem and scope
- 2
Collect licensed references
- 3
Sketches
- 4
Brand style guides and layout constraints
- 5
Then follow this sequence: 1
- OutFinish with
Reviewer-approved visual assets linked to export-ready frames
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 style set and licensed reference library; final art direction 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 reviewer-approved visual assets linked to export-ready frames 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 brand assets, authorized references 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.
How we build it
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
Scoping call
Day 1Thirty 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
MVP
6 daysOne buyer segment, one recurring use case; first modules: arrange elements on an infinite canvas workspace; manage layouts with layer-based composition. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
7 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
3 weeksSelf-serve onboarding, billing, monitoring and the wider integration set.
- 5
Run and improve
MonthlyWe 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.
- Pick the riskiest assumption. Here: will creative teams and studios producing visual artwork for print and social channels use it to solve "visual work is split across a canvas tool, a style tool and a generation tool, so files, styles and approvals drift apart"?
- Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
- Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted assets per creative hour and corrections after client approval.
- Measure, then decide. Track accepted assets per creative 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 fixed brand style set and licensed reference library; final art direction and publication checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: arrange elements on an infinite canvas workspace; manage layouts with layer-based composition. Support the remaining modules with operator review: compose scenes within frames and export selected areas; place and manipulate elements by drag and drop; convert rough sketches into refined generated art; let AI create and arrange files, notes and images on the board; translate design ideas into prompts through interactive dialogue; apply consistent styles across elements while preserving composition; offer a built-in library of visual styles; import reference images to create variations or standalone assets; insert or remove image parts without affecting the rest of the composition; export finished images at high resolution for print and social use; read the whole workspace and the relative positions of elements; organize scattered ideas, suggest directions and draft a roadmap from board content; create functional widgets such as charts or interactive elements on the canvas; hand off project plans to external agents while preserving spatial context; charge one credit per image generation, with failed generations not consuming credits. 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 export-ready frames. Retain the explicit scope boundary: One fixed brand style set and licensed reference library; final art direction 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: One fixed brand style set and licensed reference library; final art direction and publication checks remain human.
Investment
A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.
- Phase 1
MVP
One buyer segment, one recurring use case; first modules: arrange elements on an infinite canvas workspace; manage layouts with layer-based composition. Manual review in the loop.
- Phase 2
Paid pilot
Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- Phase 3
Full product
Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$44,000about 5 weeks of creation time · start with the MVP from $13,000
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.
| Stage | Hosting and infrastructure | AI usage | Total per month |
|---|---|---|---|
| MVP and paid pilotabout 3 customers | $40–$80 | $150–$310 | $190–$390 |
| Full productabout 50 customers | $160–$320 | $2,100–$4,200 | $2,260–$4,520 |
Run it or resell it
For your own team
Creative teams and studios producing visual artwork for print and social channels run it inside the business: licensed references, sketches, brand style guides and layout constraints in, reviewer-approved visual assets linked to export-ready frames out, reviewed by your people.
As part of your offer
Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.
Your brand, or this one
Run it under your own brand, or start from this concept style.
- primary
#913127 - accent
#54c9b6 - surface
#f1e6e4 - ink
#22201e
- Headings
- Libre Baskerville
- Text
- IBM Plex Sans
- Voice
- Confident, visual, craft-proud
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 export-ready frames. 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 switching and style drift while keeping one reviewed visual pipeline. Demonstrate a concrete reviewer-approved visual assets linked to export-ready frames using the buyer's approved example and show the baseline, corrections and actual delivery effort.
Where to find buyers
Creative teams and studios producing visual artwork for print and social channels 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 export-ready frames from a small authorized input set, with a transparent calculation of accepted assets per creative hour and corrections after client approval and no promised savings.
The first 30 days
- Week 1: interview five creative teams and studios producing visual artwork for print and social channels and inspect a recent example of visual work split across a canvas tool, a style tool and a generation tool, so files, styles and approvals drift apart.
- Week 2: prepare a consented or synthetic demonstration of the task modules.
- Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
- Week 4: measure accepted assets per creative 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 creative 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 creative 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 reviewer-approved visual assets linked to export-ready frames. 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 visual artwork for print and social channels. Repeatable delivery and useful integrations matter more than access to a base model.
Alternatives and positioning
CanvAi, Causal and Stylar used as separate rented subscriptions, plus freelancers and generic generation tools. Compare this product with the buyer's present method on accepted assets per creative 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, 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 export-ready frames. Track cost per accepted output, including correction work, unsuccessful cases and support.
Safeguards
Preserve brand voice, source attribution, asset accuracy and usage permissions. Clients approve substantive changes and publication scope. One fixed brand style set and licensed reference library; final art direction 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.