AI app for product development · no coding needed
Qualitative interview evidence synthesis workspace
Reduce manual coding and reporting effort while keeping every theme traceable to participant evidence.
Made for: Product researchers and research leads running interview-based studies
What it does for you
The problem
Interview recordings, transcripts, codes and reports sit in separate tools, so themes are hard to trace back to what participants actually said.
What it gives you
Reviewer-approved themes, evidence-linked reports and requirement drafts
What you give it
Licensed interview recordingstranscriptsstudy planscoding schemes
Build your own version of Insight7 3.0, Usercall AI Qualitative Analysis and more
One app with what these 10 AI tools do, yours to keep and change: Insight7 3.0, Usercall AI Qualitative Analysis, Insight7, FindOurView, User Evaluation AI, Odaptos, Searchie Copilot, Mira, URAi, Nugget AI.
Everything these tools do, in one app
- Multi-format data support Handles interview data in video, audio, and text formats.Found in Insight7 3.0, Insight7, Searchie Copilot
- Automated transcription Automatically converts audio and video content into text.Found in User Evaluation AI, Searchie Copilot, URAi and 1 more
- Automated coding and theme identification Uses AI to code data and identify themes, reducing manual effort.Found in Usercall AI Qualitative Analysis, Insight7, User Evaluation AI and 1 more
- Cross-interview synthesis Aggregates and analyzes multiple interviews to identify common themes.Found in User Evaluation AI, Nugget AI, Mira
- Visualization dashboards Provides interactive dashboards and visual representations of insights.Found in Usercall AI Qualitative Analysis, Insight7, Insight7 3.0
- Automated report generation Creates ready-to-use reports and summaries with minimal effort.Found in Insight7, URAi, Nugget AI and 1 more
- Collaboration tools Allows multiple users to work on the same project and share insights.Found in Usercall AI Qualitative Analysis, URAi
- Export options Enables exporting reports and coded data in various formats.Found in Usercall AI Qualitative Analysis
- Slack integration Integrates with Slack to query research data and involve teams in decision-making.Found in FindOurView
- Editable AI insights Allows users to manually edit AI-generated insights for accuracy.Found in FindOurView
- AI-moderated interviews Conducts user interviews automatically with dynamic questioning.Found in User Evaluation AI, Mira
- Participant recruitment Provides built-in access to a panel of participants for research studies.Found in Mira
- Non-verbal emotion analysis Captures facial expressions, voice tone, and eye tracking during interviews.Found in Mira
- Knowledge library Stores, searches, and retrieves insights for future reference.Found in URAi
- Research planning Helps organize and strategize research projects.Found in URAi
- Feedback collection at scale Gathers in-depth feedback from many participants efficiently.Found in URAi
- PRD generation Automatically generates product requirement documents with customer quotes.Found in Nugget AI
- Developer handoff integration Integrates with tools like Linear and GitHub for developer handoff.Found in Nugget AI
- MCP server for AI agents Allows AI agents to query interviews and draft specs grounded in user evidence.Found in Nugget AI
How it works, step by step
- Ingest interview video, audio and text
- Transcribe audio and video into text
- Code segments and propose themes
- Synthesize themes across multiple interviews
- Show interactive insight dashboards
- Generate draft reports and summaries
- Support multi-user projects and shared insights
- Export reports and coded data
- Answer research questions from Slack
- Let reviewers edit AI insights
- Run AI-moderated interviews with dynamic questions
- Recruit participants from a panel
- Capture facial expression, voice tone and eye-tracking signals
- Store and search a knowledge library
- Organize research plans and projects
- Collect feedback from many participants
- Draft requirement documents with customer quotes
- Push approved items to Linear and GitHub
- Expose an MCP server for agent queries
- 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 evidence package 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 Qualitative interview evidence synthesis 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 Qualitative interview evidence synthesis 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 links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data200 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 manual coding and reporting effort while keeping every theme traceable to participant evidence. For product researchers and research leads running interview-based studies, convert licensed interview recordings, transcripts, study plans and coding schemes into reviewer-approved themes, evidence-linked reports and requirement drafts. The benefit is a testable hypothesis, measured through accepted themes per analyst hour and corrections after report approval; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect licensed interview recordings, transcripts, study plans and coding schemes, then follow this sequence: 1. Ingest interview video, audio and text. 2. Transcribe audio and video into text. 3. Code segments and propose themes. 4. Synthesize themes across multiple interviews. Resolve uncertain cases with qualified reviewers, approve reviewer-approved themes, evidence-linked reports and requirement drafts, and measure accepted themes per analyst hour and corrections after report approval against a documented baseline.
How the AI works
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. Emotion and eye-tracking signals are indicative only; final theme, report and requirement decisions remain with qualified researchers. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve participant voice, source attribution, quotation accuracy and consent permissions. Researchers approve substantive theme, report and requirement changes and publication scope. One study type and one approved input format; final theme, report and requirement decisions remain with qualified researchers. 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 study type and one approved input format; final theme, report and requirement decisions remain with qualified researchers. Implement one approved input format, a bounded representative case set and the first three task modules: ingest interview video, audio and text; transcribe audio and video into text; code segments and propose themes. Support the remaining modules with operator review: synthesize themes across multiple interviews; show interactive insight dashboards; generate draft reports and summaries. 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
Participant-owned recordings, authorized transcripts and permitted research sources. Cloud asset storage, Slack, Linear, GitHub and export 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.
The screens in detail
Primary screens: Study setup and data intake, Coding and theme review, Report and handoff. Use a thumbnail gallery for studies, a large central transcript and coding canvas, and a right-hand panel for themes, evidence links and comments. Let users compare coded segments side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant quote. Make the task-specific outcome reviewer-approved themes, evidence-linked reports and requirement drafts visible beside its evidence, review state and value baseline.
