Assumption-driven product feedback decision workspace

For

Product managers and product teams deciding what to build next

Solves

Feedback sits in many tools, insights are extracted by hand, and prioritization decisions are not linked to evidence or recorded assumptions.

Delivers

Reviewed, evidence-linked build decisions with recorded assumptions

Built in

about 4 weeks of creation time, MVP in 5 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 the time from raw feedback to a reviewed, evidence-linked build decision while keeping the assumptions and their evidence visible.

1. Collect feedback from connected sources.
2. Extract insights and patterns with AI.
3. Run sentiment and keyword analysis.
4. Rank requests by customer impact and business value.
5. Record the assumptions behind each ranking.
6. Link every insight to its source evidence.
7. Generate requirement documents and user stories.
8. Run automated user interviews.
9. Analyze market trends and competitor signals.
10. Build and update the roadmap.
11. Provide customizable dashboards.
12. Support whiteboards for ideation.
13. Track goals and initiatives.
14. Enable real-time team collaboration.
15. Automate repetitive triage and routing.
16. Support multiple languages.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewed, evidence-linked build decisions with recorded assumptions with source references and unresolved questions.

Everything these tools do, in one app

• Feedback collection Gathers user feedback from multiple sources into one place.Found in Fibery 2.0, Zeda.io, AI Product Discovery by Zeda.io and 2 more
• AI insight extraction Uses AI to automatically identify key insights and patterns from feedback.Found in Fibery 2.0, Zeda.io, AI Product Discovery by Zeda.io and 1 more
• Prioritization framework Ranks feature requests based on customer impact and business value.Found in Fibery 2.0, AI Product Discovery by Zeda.io, Kraftful 3.0
• Roadmap creation Helps teams visualize and plan product timelines and progress.Found in Fibery 2.0, Zeda.io
• Customizable dashboards Provides flexible views to track product insights and metrics.Found in Zeda.io, AI Product Discovery by Zeda.io
• Integration with tools Connects with other software like development, CRM, and communication tools.Found in Fibery 2.0, Zeda.io, AI Product Discovery by Zeda.io and 1 more
• Automated user interviews Conducts user interviews automatically without manual scheduling.Found in Kraftful 3.0, Jo
• AI-generated documents Automatically creates product requirement documents and user stories from feedback.Found in Kraftful 3.0
• Sentiment analysis Analyzes text to determine customer sentiment and trends.Found in MonkeyLearn
• Keyword extraction Identifies key terms and topics from customer feedback.Found in MonkeyLearn
• Market trend analysis Analyzes real-time market data to identify trending products.Found in Supascout
• Competitive intelligence Reveals competitors' pricing and sales strategies.Found in Supascout
• Content generation Generates text content with customizable tone and style.Found in YOMO
• Real-time collaboration Enables team members to work together simultaneously.Found in YOMO
• Workflow automation Automates repetitive tasks to improve efficiency.Found in YOMO
• Multi-language support Supports content creation and analysis in multiple languages.Found in YOMO
• Whiteboards Provides interactive whiteboards for ideation and planning.Found in Fibery 2.0
• Goal tracking Sets and tracks goals and initiatives to align with strategy.Found in Zeda.io

What goes in, what comes out

The workflow

1. In
   Start with

   Collected user feedback, interview transcripts, support tickets, CRM notes and market data

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect collected user feedback

4. 3

   Interview transcripts

5. 4

   Support tickets

6. 5

   CRM notes and market data

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewed, evidence-linked build decisions with recorded assumptions

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 feedback taxonomy and approved source list; final prioritization and roadmap decisions remain with the product owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Feedback inbox and sources, Insight and assumption review, Decision and roadmap view. Use a source list for connected feedback channels, a central review canvas for insights and assumptions, and a right-hand panel for evidence, sentiment, priority and comments. Let users compare candidate decisions side by side. Display draft, changes requested and approved states. Provide a shareable decision record with comments anchored to the relevant insight. Make the task-specific outcome reviewed, evidence-linked build decisions with recorded assumptions visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source connections, insight 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 feedback exports, authorized interview recordings and permitted market sources. Support desks, CRM, development trackers, communication tools and cloud storage. 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

   5 days

   One buyer segment, one recurring use case; first modules: collect feedback from connected sources; extract insights and patterns with AI. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 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 deciding what to build next use it to solve "feedback sits in many tools, insights are extracted by hand, and prioritization decisions are not linked to evidence or recorded assumptions"?
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: Reviewed decisions per product hour and rework after roadmap approval.
4. Measure, then decide. Track reviewed decisions per product hour and rework after roadmap 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 feedback taxonomy and approved source list; final prioritization and roadmap decisions remain with the product owner. Implement one approved input format, a bounded representative case set and the first two task modules: collect feedback from connected sources; extract insights and patterns with AI. Support the third module with operator review: run sentiment and keyword analysis. 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, evidence-linked build decisions with recorded assumptions. Retain the explicit scope boundary: One fixed feedback taxonomy and approved source list; final prioritization and roadmap decisions remain with the product owner.

What the build depends on. Feedback upload and preview, asynchronous analysis jobs, editable version history, reviewer access and tested export formats. High-fidelity prioritization requires product-owner judgment. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed feedback taxonomy and approved source list; final prioritization and roadmap decisions remain with the product owner.

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 feedback 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, evidence-linked build decisions with recorded assumptions. 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 the time from raw feedback to a reviewed, evidence-linked build decision while keeping the assumptions and their evidence visible. Demonstrate a concrete reviewed, evidence-linked build decisions with recorded assumptions using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product managers and product teams deciding what to build next professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, evidence-linked build decisions with recorded assumptions from a small authorized input set, with a transparent calculation of reviewed decisions per product hour and rework after roadmap approval and no promised savings.

The first 30 days

1. Week 1: interview five product managers and product teams deciding what to build next and inspect a recent example of feedback sits in many tools, insights are extracted by hand, and prioritization decisions are not linked to evidence or recorded assumptions.
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 reviewed decisions per product hour and rework after roadmap 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: Reviewed decisions per product hour and rework after roadmap 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

Reviewed decisions per product hour and rework after roadmap approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, evidence-linked build decisions with recorded assumptions. 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 feedback taxonomies, prioritization 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 deciding what to build next. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Fibery 2.0, Zeda.io, YOMO, AI Product Discovery by Zeda.io, Kraftful 3.0, MonkeyLearn, Supascout and Jo, plus spreadsheets and manual triage. Compare this product with the buyer's present method on reviewed decisions per product hour and rework after roadmap approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, storage, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, evidence-linked build decisions with recorded assumptions. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, consent for interview data and usage permissions. Product owners approve substantive prioritization and roadmap changes. One fixed feedback taxonomy and approved source list; final prioritization and roadmap decisions remain with the product owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.