Customer conversation insight and backlog workbench

For

Product and customer experience teams analyzing customer conversations and feedback

Solves

Customer conversations and feedback sit in separate tools, so insights, sentiment and backlog requests are extracted by hand and lose their evidence.

Delivers

Reviewer-approved insights, categorized feedback and prioritized backlog requests linked to source evidence

Built in

about 5 weeks of creation time, MVP in 6 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 manual feedback triage while keeping every insight linked to its source conversation.

1. Ingest conversations and feedback from connected sources.
2. Transcribe recordings in supported languages.
3. Detect and label speakers in transcripts.
4. Apply custom vocabulary for transcription accuracy.
5. Evaluate customer sentiment per conversation and segment.
6. Categorize feedback into requests, defects, praises and learnings.
7. Generate summaries and insights from analyzed data.
8. Consolidate all sources into one feedback hub.
9. Search and filter high-priority conversations and cohorts.
10. Visualize trends and comparative analyses.
11. Generate data visualizations for trend digestion.
12. Produce research reports and presentations.
13. Provide guided analysis through a copilot.
14. Offer customized recommendations from findings.
15. Save, group and track conversations.
16. Push prioritized backlog requests to Slack, JIRA and Linear.
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 reviewer-approved insight set with source references and unresolved questions.

Everything these tools do, in one app

• Conversation analysis Analyzes customer conversations and feedback to extract insights.Found in User Evaluation, Inari, impaction.ai
• AI-generated insights Automatically generates summaries and insights from data.Found in User Evaluation, Inari
• Sentiment evaluation Automatically evaluates customer sentiment.Found in Inari
• Feedback categorization Categorizes feedback into top requests, defects, praises, and learnings.Found in Inari
• Unified feedback hub Consolidates data from multiple sources into a single hub.Found in Inari
• Multi-source data support Works with various data sources like AWS S3, GCP BigQuery, PostgreSQL, MySQL.Found in impaction.ai
• Multilingual transcription Provides accurate transcriptions in over 57 languages.Found in User Evaluation
• Speaker detection Identifies different speakers in transcriptions.Found in User Evaluation
• Custom vocabulary Allows custom vocabulary for transcription accuracy.Found in User Evaluation
• Real-time trend visualization Provides dynamic visualizations of trends and product insights.Found in Inari
• Generative data visualizations Creates generative data visualizations to help digest trends and comparative analyses.Found in User Evaluation
• Research reports Generates detailed research reports and presentations.Found in User Evaluation
• Powerful search Equipped with an intuitive search toolkit to identify high-priority conversations and deep dive into data cohorts.Found in impaction.ai
• Columbus Copilot Provides guided analysis through Columbus Copilot.Found in impaction.ai
• Customized recommendations Offers customized recommendations.Found in impaction.ai
• Save, group, track conversations Allows users to save, group, and track conversations.Found in impaction.ai
• Backlog management integration Pushes insights and prioritized backlog requests to Slack, JIRA, and Linear.Found in Inari
• Seamless integration Integrates with over 100 applications.Found in User Evaluation

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted conversation recordings, transcripts, support tickets, survey responses and review exports

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted conversation recordings

4. 3

   Transcripts

5. 4

   Support tickets

6. 5

   Survey responses and review exports

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewer-approved insights, categorized feedback and prioritized backlog requests 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. Supported languages, custom vocabulary and source connectors remain bounded; final categorization and backlog priority decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source and consent setup, Analysis workspace, Insight and backlog review. Use a thumbnail gallery for projects, a large central analysis canvas, and a right-hand panel for sources, categories and comments. Let users compare cohorts and time periods side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant conversation. Make the task-specific outcome reviewer-approved insights, categorized feedback and prioritized backlog requests linked to source evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, client comments, approval states, usage allowances, retention 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 conversation recordings, authorized support tickets, survey responses and review exports. Cloud storage, data warehouses, backlog tools and messaging destinations. Start with file exchange and validate destination specifications before promising direct backlog writes. 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

   6 days

   One buyer segment, one recurring use case; first modules: ingest conversations and feedback from connected sources; transcribe recordings in supported languages. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

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

4. 4

   Full product

   3 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 and customer experience teams analyzing customer conversations and feedback use it to solve "customer conversations and feedback sit in separate tools, so insights, sentiment and backlog requests are extracted by hand and lose their evidence"?
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 insights per analyst hour and backlog items traced to source evidence.
4. Measure, then decide. Track accepted insights per analyst hour and backlog items traced to source evidence; 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 approved source set, one supported language and one backlog destination; final categorization and priority decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: ingest conversations and feedback from connected sources; transcribe recordings in supported languages. Support the remaining modules with operator review: speaker detection, custom vocabulary, sentiment evaluation, feedback categorization, insight generation, unified hub, search, trend visualization, generative visualizations, research reports, copilot guidance, recommendations, conversation tracking and backlog push. 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, languages and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved insights, categorized feedback and prioritized backlog requests linked to source evidence. Retain the explicit scope boundary: One approved source set, one supported language and one backlog destination; final categorization and priority decisions remain human.

What the build depends on. Source upload and preview, asynchronous analysis jobs, editable version history, reviewer access and tested export formats. High-fidelity analysis requires specialist research QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved source set, one supported language and one backlog destination; final categorization and priority decisions remain human.

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 source set. Offer a monthly analysis allowance after repeat demand. Quote complex multi-source or multilingual work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved insight set. 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 feedback triage while keeping every insight linked to its source conversation. Demonstrate a concrete reviewer-approved insight set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and customer experience teams professional communities; specialist research consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved insight set from a small authorized input set, with a transparent calculation of accepted insights per analyst hour and backlog items traced to source evidence and no promised savings.

The first 30 days

1. Week 1: interview five product and customer experience teams analyzing customer conversations and feedback and inspect a recent example of customer conversations and feedback sitting in separate tools, so insights, sentiment and backlog requests are extracted by hand and lose their evidence.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted insights per analyst hour and backlog items traced to source evidence, 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 insights per analyst hour and backlog items traced to source evidence. 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 insights per analyst hour and backlog items traced to source evidence; 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 insights, categorized feedback and prioritized backlog requests 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 categories, review examples and verified source connectors, together with reliable delivery for a narrow product-research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and customer experience teams analyzing customer conversations and feedback. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

User Evaluation, Inari and impaction.ai, plus manual spreadsheet triage and internal scripts. Compare this product with the buyer's present method on accepted insights per analyst hour and backlog items traced to source evidence. 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 attempts, 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 insights, categorized feedback and prioritized backlog requests 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. Named reviewers approve substantive categorizations and backlog priorities. One approved source set, one supported language and one backlog destination; final categorization and priority decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.