Customer feedback evidence and reporting workspace

For

Product, marketing and research teams that collect and analyze customer feedback

Solves

Feedback sits in separate interview, survey, dashboard and reporting tools, so teams cannot trace a conclusion back to the customer statement behind it.

Delivers

Reviewer-approved findings linked to source evidence

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce the effort to turn collected feedback into reviewed, source-linked findings.

1. Collect feedback through AI interviews, surveys and conversations.
2. Run adaptive voice or chat interviews that follow up on responses.
3. Transcribe interview audio into text.
4. Categorize incoming feedback into topics and themes.
5. Detect positive, neutral and negative sentiment.
6. Show real-time analytics as data arrives.
7. Generate reports from customizable templates.
8. Display interactive dashboards with filters.
9. Aggregate data from spreadsheets, databases and cloud sources.
10. Identify patterns and forecast trends from collected data.
11. Let team members share reports and comment on findings.
12. Connect third-party tools for import and export.
13. Configure workflows and automation per study.
14. Automate routine sorting, tagging and notification tasks.
15. Suggest next questions and themes from current context.
16. Support multi-language interviews and responses.
17. Recruit participants and manage voice projects from one dashboard.
18. Capture voice and video of users performing tasks.
19. Tailor AI interviewers to adapt questions to responses.
20. Alert teams about new feedback or issues.
21. Let teams reply to customer concerns from the platform.
22. Summarize conversations and answer natural-language queries with explanations.
23. Compare the reviewed result with the recorded baseline and value assumptions.
24. Capture corrections and named-owner approval before consequential use.
25. Export a versioned reviewer-approved findings linked to source evidence with source references and unresolved questions.

Everything these tools do, in one app

• AI-driven feedback collection Automatically gathers feedback from users through AI-powered interviews, surveys, or conversations.Found in TheySaid 3.0, RevealAI, UserCall
• Conversational AI interviews Conducts interactive voice or chat interviews that adapt to user responses.Found in TheySaid 3.0, RevealAI, UserCall
• Automated feedback categorization Sorts incoming feedback into relevant topics or themes without manual effort.Found in FeedbackStream
• Sentiment analysis Detects positive, neutral, or negative tones in customer responses.Found in FeedbackStream
• Real-time analytics Provides immediate insights and performance tracking as data is collected.Found in Voicepanel, RevealAI, Insights Hub
• Automated report generation Creates reports with customizable templates based on collected data.Found in Insights Hub
• Interactive dashboards Visualizes data in real-time through customizable dashboards.Found in Arro, FeedbackStream, Insights Hub
• Data aggregation Collects and combines data from multiple sources like spreadsheets, databases, and cloud services.Found in Insights Hub
• AI-powered trend analysis Identifies patterns and predicts future trends using AI.Found in Insights Hub
• Collaboration tools Enables team members to share reports, insights, and work together on projects.Found in Arro, Aptitude, Insights Hub
• Integration with other platforms Connects with popular third-party tools and services for seamless workflow.Found in Arro, TheySaid 3.0, Voicepanel and 3 more
• Customizable workflows Allows users to tailor processes and automation to fit specific needs.Found in Arro, Aptitude
• Task automation Automates routine tasks to save time and reduce manual effort.Found in Arro, Aptitude
• Context-aware suggestions Provides intelligent recommendations based on the current context to aid decision-making.Found in Arro
• Secure messaging Offers end-to-end encryption for confidential communication.Found in Convo
• Smart message suggestions Suggests replies to speed up typing and response times.Found in Convo
• Conversation summaries Generates context-aware summaries to keep everyone updated.Found in Convo
• Natural language understanding Supports conversational queries and understands user intent.Found in Whyser
• Detailed explanations Provides clear, context-aware answers to complex questions.Found in Whyser
• Multi-language support Accommodates diverse user bases by supporting multiple languages and dialects.Found in UserCall
• Automatic transcription Converts audio from interviews into text transcripts automatically.Found in UserCall
• Panel recruiting Helps recruit participants for research studies or feedback sessions.Found in TheySaid 3.0
• Usability testing Captures voice and video of users performing tasks to evaluate usability.Found in TheySaid 3.0
• Customizable AI agents Allows tailoring of AI interviewers to adapt questions based on responses.Found in UserCall
• Real-time alerts Notifies teams promptly about new feedback or issues.Found in FeedbackStream
• Response management Enables teams to reply to customer concerns directly from the platform.Found in FeedbackStream
• Voice project management Manages multiple voice projects from a single dashboard.Found in Voicepanel
• Customizable templates Provides templates to speed up content creation or report generation.Found in Voicepanel, Insights Hub
• Predictive insights Uses AI to forecast trends and outcomes based on data.Found in Insights Hub

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted interview transcripts, survey responses, support messages and usage notes

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted interview transcripts

4. 3

   Survey responses

5. 4

   Support messages and usage notes

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved findings linked to source evidence

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 study type and permitted source set; final interpretation and reporting decisions remain with the research team. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Study setup and sources, Editable analysis workspace, Client report and delivery. Use a thumbnail gallery for studies, a large central analysis canvas, and a right-hand panel for sources, themes and comments. Let users compare theme versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant finding. Make the task-specific outcome reviewer-approved findings linked to source evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, 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

Customer-owned transcripts, authorized interviews and permitted research sources. Cloud storage, survey and support-desk import/export and reporting 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: collect feedback through AI interviews, surveys and conversations; run adaptive voice or chat interviews that follow up on responses. 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, marketing and research teams that collect and analyze customer feedback use it to solve "feedback sits in separate interview, survey, dashboard and reporting tools, so teams cannot trace a conclusion back to the customer statement behind it"?
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 findings per analyst hour and corrections after report approval.
4. Measure, then decide. Track accepted findings 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 fixed study type and permitted source set; final interpretation and reporting decisions remain with the research team. Implement one approved input format, a bounded representative case set and the first two task modules: collect feedback through AI interviews, surveys and conversations; run adaptive voice or chat interviews that follow up on responses. Support the remaining modules with operator review. 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 findings linked to source evidence. Retain the explicit scope boundary: One fixed study type and permitted source set; final interpretation and reporting decisions remain with the research team.

What the build depends on. Asset upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity research requires specialist qualitative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed study type and permitted source set; final interpretation and reporting decisions remain with the research team.

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 video research separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved findings linked to source evidence. 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 effort to turn collected feedback into reviewed, source-linked findings. Demonstrate a concrete reviewer-approved findings linked to source evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product, marketing and research teams that collect and analyze customer feedback 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 findings linked to source evidence from a small authorized input set, with a transparent calculation of accepted findings per analyst hour and corrections after report approval and no promised savings.

The first 30 days

1. Week 1: interview five product, marketing and research teams that collect and analyze customer feedback and inspect a recent example of feedback sitting in separate interview, survey, dashboard and reporting tools.
2. Week 2: prepare a consented or synthetic demonstration of the stated task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted findings 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 findings 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 findings 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 findings linked to source evidence. 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 study templates, coding schemes 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, marketing and research teams that collect and analyze customer feedback. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Arro, TheySaid 3.0, Voicepanel, RevealAI, Aptitude, Convo, Whyser, FeedbackStream, UserCall and Insights Hub. Compare this product with the buyer's present method on accepted findings 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

Interview generation attempts, transcription and video 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 reviewer-approved findings linked to source evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve participant consent, source attribution, quotation accuracy and usage permissions. Research owners approve substantive findings and publication scope. One fixed study type and permitted source set; final interpretation and reporting decisions remain with the research team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.