Qualitative interview evidence synthesis workspace

For

Product researchers and research leads running interview-based studies

Solves

Interview recordings, transcripts, codes and reports sit in separate tools, so themes are hard to trace back to what participants actually said.

Delivers

Reviewer-approved themes, evidence-linked reports and requirement drafts

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce manual coding and reporting effort while keeping every theme traceable to participant evidence.

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.
5. Show interactive insight dashboards.
6. Generate draft reports and summaries.
7. Support multi-user projects and shared insights.
8. Export reports and coded data.
9. Answer research questions from Slack.
10. Let reviewers edit AI insights.
11. Run AI-moderated interviews with dynamic questions.
12. Recruit participants from a panel.
13. Capture facial expression, voice tone and eye-tracking signals.
14. Store and search a knowledge library.
15. Organize research plans and projects.
16. Collect feedback from many participants.
17. Draft requirement documents with customer quotes.
18. Push approved items to Linear and GitHub.
19. Expose an MCP server for agent queries.
20. Compare the reviewed result with the recorded baseline and value assumptions.
21. Capture corrections and named-owner approval before consequential use.
22. Export a versioned reviewer-approved evidence package with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed interview recordings, transcripts, study plans and coding schemes

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed interview recordings

4. 3

   Transcripts

5. 4

   Study plans and coding schemes

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved themes, evidence-linked reports and requirement drafts

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. 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.

What your team sees

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.

Accounts and administration

Project ownership, asset versions, participant consent records, 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

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.

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: ingest interview video, audio and text; transcribe audio and video into text; code segments and propose themes. 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 researchers and research leads running interview-based studies use it to solve "interview recordings, transcripts, codes and reports sit in separate tools, so themes are hard to trace back to what participants actually said"?
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 themes per analyst hour and corrections after report approval.
4. Measure, then decide. Track accepted themes per analyst hour and corrections after report 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 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.

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 reviewer-approved themes, evidence-linked reports and requirement drafts. Retain the explicit scope boundary: One study type and one approved input format; final theme, report and requirement decisions remain with qualified researchers.

What the build depends on. Asset upload and preview, asynchronous transcription jobs, editable version history, reviewer access and tested export formats. High-fidelity research requires qualified human review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One study type and one approved input format; final theme, report and requirement decisions remain with qualified researchers.

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 study package. Offer a monthly production allowance after repeat demand. Quote complex multi-language or specialist research separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved themes, evidence-linked reports and requirement drafts. 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 manual coding and reporting effort while keeping every theme traceable to participant evidence. Demonstrate a concrete reviewer-approved themes, evidence-linked reports and requirement drafts using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product researchers and research leads running interview-based studies professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved themes, evidence-linked reports and requirement drafts from a small authorized input set, with a transparent calculation of accepted themes per analyst hour and corrections after report approval and no promised savings.

The first 30 days

1. Week 1: interview five product researchers and research leads running interview-based studies and inspect a recent example of interview recordings, transcripts, codes and reports sitting in separate tools, so themes are hard to trace back to what participants actually said.
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 themes per analyst hour and corrections after report 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: Accepted themes per analyst hour and corrections after report 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

Accepted themes per analyst hour and corrections after report approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved themes, evidence-linked reports and requirement drafts. 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 coding schemes, study templates and review examples, together with reliable delivery for a narrow research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product researchers and research leads running interview-based studies. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Insight7 3.0, Usercall AI Qualitative Analysis, Insight7, FindOurView, User Evaluation AI, Odaptos, Searchie Copilot, Mira, URAi and Nugget AI, plus manual spreadsheet coding and generic transcription tools. Compare this product with the buyer's present method on accepted themes per analyst hour and corrections after report approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Transcription and model processing, storage, reviewer hours, participant incentives, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved themes, evidence-linked reports and requirement drafts. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.