AI app for product development · no coding needed
Assumption-driven product feedback decision workspace
Reduce the time from raw feedback to a reviewed, evidence-linked build decision while keeping the assumptions and their evidence visible.
Made for: Product managers and product teams deciding what to build next
What it does for you
The problem
Feedback sits in many tools, insights are extracted by hand, and prioritization decisions are not linked to evidence or recorded assumptions.
What it gives you
Reviewed, evidence-linked build decisions with recorded assumptions
What you give it
Collected user feedbackinterview transcriptssupport ticketsCRM notesmarket data
Build your own version of Fibery 2.0, Zeda.io and more
One app with what these 8 AI tools do, yours to keep and change: Fibery 2.0, Zeda.io, YOMO, AI Product Discovery by Zeda.io, Kraftful 3.0, MonkeyLearn, Supascout, Jo.
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
How it works, step by step
- Collect feedback from connected sources
- Extract insights and patterns with AI
- Run sentiment and keyword analysis
- Rank requests by customer impact and business value
- Record the assumptions behind each ranking
- Link every insight to its source evidence
- Generate requirement documents and user stories
- Run automated user interviews
- Analyze market trends and competitor signals
- Build and update the roadmap
- Provide customizable dashboards
- Support whiteboards for ideation
- Track goals and initiatives
- Enable real-time team collaboration
- Automate repetitive triage and routing
- Support multiple languages
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed, evidence-linked build decisions with recorded assumptions with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Assumption-driven product feedback decision workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Assumption-driven product feedback decision workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data197 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce the time from raw feedback to a reviewed, evidence-linked build decision while keeping the assumptions and their evidence visible. For product managers and product teams deciding what to build next, convert collected user feedback, interview transcripts, support tickets, CRM notes and market data into reviewed, evidence-linked build decisions with recorded assumptions. The benefit is a testable hypothesis, measured through reviewed decisions per product hour and rework after roadmap approval; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect collected user feedback, interview transcripts, support tickets, CRM notes and market data, then follow this sequence: 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. Resolve uncertain cases with qualified reviewers, approve reviewed, evidence-linked build decisions with recorded assumptions, and measure reviewed decisions per product hour and rework after roadmap approval against a documented baseline.
How the AI works
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.
Safeguards
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.
What to build first
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.
What it can connect to
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.
The screens in detail
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.
