Assumption-driven planning and decision workspace

For

Product teams and innovation leads turning ideas and project information into organized, shared visual work

Solves

Assumptions stay hidden in scattered notes and tools, so decisions lack visible evidence and shared context.

Delivers

Reviewed decision workspace with scored assumptions and source-linked evidence

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

Reduce decision rework while keeping assumptions and evidence visible.

1. Arrange project information on a shared visual canvas.
2. Edit the same canvas with multiple people in real time.
3. Track project data in tables and timelines.
4. Turn unstructured notes into formatted briefs and reports.
5. Convert sticky note ideas into interactive prototypes.
6. Flag assumptions as risky until enough evidence supports them.
7. Evaluate evidence with a separate AI service from the one that generated the assumption.
8. Surface evidence, key metrics, progress scores and risks behind each suggestion.
9. Ground frameworks in established methodologies such as Lean Canvas and MBM.
10. Maintain a proprietary memory layer that learns without context degradation.
11. Integrate client conversations into the workspace.
12. Control access and feedback through role-based permissions.
13. Support AI generative media alongside digital whiteboards.
14. Connect to cloud accounts to visualize setups and estimate costs.
15. Create and edit images and videos inside the workspace.
16. Link preferred AI services such as OpenAI and Azure.
17. Budget tokens at the project level.
18. Compare the reviewed result with the recorded baseline and value assumptions.
19. Capture corrections and named-owner approval before consequential use.
20. Export a versioned reviewed decision workspace with source references and unresolved questions.

Everything these tools do, in one app

• Shared visual canvas Gives teams one visual space to arrange and view project information together.Found in Miro 2.0 The Innovation Workspace, siift, Graphis
• Real-time collaborative editing Lets multiple people work on the same canvas at the same time.Found in Graphis
• Tables and timelines Shows project data in tables and timelines to track progress and organize tasks.Found in Miro 2.0 The Innovation Workspace
• Automated document creation Turns unstructured notes into formatted documents such as briefs and reports.Found in Miro 2.0 The Innovation Workspace
• AI-powered prototyping Converts sticky note ideas into interactive prototypes for faster feedback.Found in Miro 2.0 The Innovation Workspace
• Assumption risk scoring Flags assumptions as risky until enough evidence supports them.Found in siift
• Independent evidence evaluation Uses a separate AI service to evaluate evidence independently from the AI that generated the assumption.Found in siift
• Decision intelligence engine Surfaces evidence behind suggestions, key metrics, progress scores, and risks.Found in siift
• Framework grounding References established methodologies such as Lean Canvas and MBM.Found in siift
• Proprietary memory system Uses a patent-pending AI memory layer designed to learn and evolve without context degradation.Found in siift
• Client communication Integrates client conversations into the workspace.Found in Graphis
• Role-based permissions Controls access and feedback through role-based permissions.Found in Graphis
• AI generative media support Supports AI generative media alongside digital whiteboards.Found in Graphis
• Cloud infrastructure visualization Connects to AWS accounts to generate visual representations of cloud setups and estimate costs.Found in Miro 2.0 The Innovation Workspace
• Adobe Express integration Allows creating and editing images and videos directly within the workspace.Found in Miro 2.0 The Innovation Workspace
• Custom AI model support Links preferred AI services such as OpenAI and Azure.Found in Miro 2.0 The Innovation Workspace
• Project-level token budgeting Planned feature to budget tokens at the project level.Found in Graphis

What goes in, what comes out

The workflow

1. In
   Start with

   Notes, project data, client conversations and linked AI services

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect notes

4. 3

   Project data

5. 4

   Client conversations and linked AI services

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed decision workspace with scored assumptions and source-linked evidence

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 a separate AI service to evaluate evidence independently from the AI that generated the assumption. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed workspace schema and linked AI services; final decision and evidence checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Assumption board and canvas, Evidence and decision review, Client and delivery view. Use a thumbnail gallery for projects, a large central canvas with tables and timelines, and a right-hand panel for assumptions, evidence, risks 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 assumption or asset. Make the task-specific outcome reviewed decision workspace with scored assumptions and source-linked evidence 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

Customer-owned notes, project data and client conversations. Cloud asset storage, design-file import/export, cloud account connections and preferred AI services such as OpenAI and Azure. 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: arrange project information on a shared visual canvas; edit the same canvas with multiple people in real time. 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 product teams and innovation leads turning ideas and project information into organized, shared visual work use it to solve "assumptions stay hidden in scattered notes and tools, so decisions lack visible evidence and shared context"?
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: Decisions closed per planning cycle and assumptions retired with evidence.
4. Measure, then decide. Track decisions closed per planning cycle and assumptions retired with evidence; 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 workspace schema and linked AI services; final decision and evidence checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: arrange project information on a shared visual canvas; edit the same canvas with multiple people in real time. Support the remaining modules with operator review: track project data in tables and timelines; turn unstructured notes into formatted briefs and reports; convert sticky note ideas into interactive prototypes; flag assumptions as risky until enough evidence supports them; evaluate evidence with a separate AI service; surface evidence, key metrics, progress scores and risks. 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 decision workspace with scored assumptions and source-linked evidence. Retain the explicit scope boundary: One fixed workspace schema and linked AI services; final decision and evidence 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 planning QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed workspace schema and linked AI services; final decision and evidence 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 project package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist media separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed decision workspace with scored assumptions and source-linked evidence. 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 decision rework while keeping assumptions and evidence visible. Demonstrate a concrete reviewed decision workspace with scored assumptions and source-linked evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product teams and innovation leads turning ideas and project information into organized, shared visual work professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed decision workspace with scored assumptions and source-linked evidence from a small authorized input set, with a transparent calculation of decisions closed per planning cycle and assumptions retired with evidence and no promised savings.

The first 30 days

1. Week 1: interview five product teams and innovation leads turning ideas and project information into organized, shared visual work and inspect a recent example of assumptions stay hidden in scattered notes and tools, so decisions lack visible evidence and shared context.
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 decisions closed per planning cycle and assumptions retired with evidence, 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: Decisions closed per planning cycle and assumptions retired with evidence. 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

Decisions closed per planning cycle and assumptions retired with evidence; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed decision workspace with scored assumptions and source-linked evidence. 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 frameworks, decision constraints and review examples, together with reliable delivery for a narrow planning niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product teams and innovation leads turning ideas and project information into organized, shared visual work. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Miro 2.0 The Innovation Workspace, siift, Graphis, generic whiteboards and separate document tools. Compare this product with the buyer's present method on decisions closed per planning cycle and assumptions retired with evidence. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, media processing, storage, reviewer hours, client revision rounds and linked AI service usage. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed decision workspace with scored assumptions and source-linked evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and decision scope. One fixed workspace schema and linked AI services; final decision and evidence checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.