Assumption-driven product requirements and story workspace

For

Product managers and product teams turning assumptions and research into reviewed stories and requirements

Solves

Stories, requirements, personas, maps and usage evidence live in separate rented tools, so traceability and review break down.

Delivers

Reviewed, traceable stories and requirements with acceptance criteria

Built in

about 5 weeks of creation time, MVP in 6 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 requirement rework while keeping every story traceable to its source assumption.

1. Generate stories and requirements from supplied briefs and notes.
2. Apply customizable templates for project types and industries.
3. Let team members comment and refine content in one workspace.
4. Export content in agreed formats for sharing and editing.
5. Generate acceptance criteria alongside each story.
6. Detail user personas and craft narratives for research.
7. Explore product concepts and candidate features during brainstorming.
8. Organize stories visually in a story map.
9. Extract requirements from supplied documents and communications.
10. Track requirement status and dependencies in a traceability matrix.
11. Keep version history and change tracking for requirements.
12. Validate requirements for completeness and consistency.
13. Build maps from natural language commands.
14. Produce interactive maps that can be customized and embedded.
15. Update maps in real time as users modify them.
16. Capture session recordings to observe interactions and pain points.
17. Visualize clicks, scrolls and mouse movements as heatmaps.
18. Track conversion funnels and detect drop-off points.
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 reviewed, traceable stories and requirements with acceptance criteria with source references and unresolved questions.

Everything these tools do, in one app

• AI-assisted content generation Uses AI to generate text such as stories, user stories, or requirements from user input.Found in Story Maker by Gluecharm, StoriesOnBoard AI User Story Generator, User Story Generator and 1 more
• Customizable templates Provides templates that can be adapted to different genres, project types, or industries.Found in Story Maker by Gluecharm, StoriesOnBoard AI User Story Generator, User Story Generator and 2 more
• Collaborative workspace Allows multiple team members to contribute, comment, and refine content in real time.Found in StoriesOnBoard AI User Story Generator, Requstory, Requirements AI
• Export and sharing Enables exporting content in various formats for easy sharing and further editing.Found in Story Maker by Gluecharm, Mappie AI (Beta)
• Acceptance criteria generation Automatically generates acceptance criteria alongside user stories to enhance completeness.Found in StoriesOnBoard AI User Story Generator
• User personas development Helps detail user personas and craft narratives for UX research.Found in User Story Generator
• Concept exploration Supports teams in considering product concepts and potential features during brainstorming.Found in User Story Generator
• Story mapping integration Integrates with story mapping workflows to organize user stories visually.Found in StoriesOnBoard AI User Story Generator
• Automated requirements extraction Automatically extracts requirements from documents and communications.Found in Requstory
• Traceability matrix Tracks requirement status and dependencies to ensure clear traceability.Found in Requstory
• Version control and change tracking Monitors updates and maintains a history of changes to requirements.Found in Requirements AI
• AI-assisted requirement validation Uses AI to validate requirements for completeness and consistency.Found in Requirements AI
• Natural language map input Generates maps from natural language commands without technical expertise.Found in Mappie AI (Beta)
• Interactive map outputs Produces interactive maps that can be customized and embedded.Found in Mappie AI (Beta)
• Real-time map updates Updates maps in real time based on user modifications.Found in Mappie AI (Beta)
• Session recording Captures real-time user sessions to observe interactions and identify pain points.Found in UserTale
• Heatmaps Visualizes user clicks, scrolls, and mouse movements to understand engagement patterns.Found in UserTale
• Conversion funnels Tracks and analyzes user flow through conversion paths to detect drop-off points.Found in UserTale

What goes in, what comes out

The workflow

1. In
   Start with

   Product briefs, interview notes, session recordings and existing backlogs

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect product briefs

4. 3

   Interview notes

5. 4

   Session recordings and existing backlogs

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, traceable stories and requirements with acceptance criteria

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 template set and one agreed export schema; final product decisions and prioritization remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Assumption and source intake, Editable story and requirements workspace, Review and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for sources, assumptions, dependencies and comments. Let users compare story versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant story or map node. Make the task-specific outcome reviewed, traceable stories and requirements with acceptance criteria visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, team comments, approval states, usage allowances, revision limits, export 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

Product-owned briefs, authorized interview notes and permitted research sources. Backlog and issue-tracker import/export, design-file import/export and documentation 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

   6 days

   One buyer segment, one recurring use case; first modules: generate stories and requirements from supplied briefs and notes; apply customizable templates for project types and industries. 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 managers and product teams turning assumptions and research into reviewed stories and requirements use it to solve "stories, requirements, personas, maps and usage evidence live in separate rented tools, so traceability and review break down"?
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 stories per planning hour and requirement changes after development start.
4. Measure, then decide. Track accepted stories per planning hour and requirement changes after development start; 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 template set and one agreed export schema; final product decisions and prioritization remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate stories and requirements from supplied briefs and notes; apply customizable templates for project types and industries. Support the remaining modules with operator review: collaborative workspace, acceptance criteria generation, requirements extraction, traceability matrix and validation. 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, traceable stories and requirements with acceptance criteria. Retain the explicit scope boundary: One fixed template set and one agreed export schema; final product decisions and prioritization remain human.

What the build depends on. Source upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity requirements work requires specialist product QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed template set and one agreed export schema; final product decisions and prioritization 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 requirements package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist research separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, traceable stories and requirements with acceptance criteria. 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 requirement rework while keeping every story traceable to its source assumption. Demonstrate a concrete reviewed, traceable stories and requirements with acceptance criteria using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product managers and product teams turning assumptions and research into reviewed stories and requirements professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, traceable stories and requirements with acceptance criteria from a small authorized input set, with a transparent calculation of accepted stories per planning hour and requirement changes after development start and no promised savings.

The first 30 days

1. Week 1: interview five product managers and product teams turning assumptions and research into reviewed stories and requirements and inspect a recent example of stories, requirements, personas, maps and usage evidence living in separate rented tools, so traceability and review break down.
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 stories per planning hour and requirement changes after development start, 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 stories per planning hour and requirement changes after development start. 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 stories per planning hour and requirement changes after development start; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, traceable stories and requirements with acceptance criteria. 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 templates, traceability rules and review examples, together with reliable delivery for a narrow product niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product managers and product teams turning assumptions and research into reviewed stories and requirements. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Story Maker by Gluecharm, UserTale, StoriesOnBoard AI User Story Generator, User Story Generator, Mappie AI (Beta), Requstory and Requirements AI. Compare this product with the buyer's present method on accepted stories per planning hour and requirement changes after development start. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, session-recording processing, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, traceable stories and requirements with acceptance criteria. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve product intent, source attribution, quotation accuracy and usage permissions. Product owners approve substantive changes and release scope. One fixed template set and one agreed export schema; final product decisions and prioritization remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.