AI interaction evidence review and QA workspace

For

AI product teams and QA engineers testing and improving AI systems and text

Solves

AI systems and text are tested in scattered tools, so interaction evidence, error corrections and fix decisions are not kept in one reviewable place.

Delivers

Reviewer-approved interaction evidence linked to test cases

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$12,500 for the MVP, $42,500 for the full product

Run it

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

Reduce review cycles while keeping every AI interaction correction traceable.

1. Process interaction data with customizable workflows.
2. Show real-time analytics and reporting in a dashboard.
3. Integrate with common business software.
4. Support growing data volume with a scalable architecture.
5. Provide predictive insights from built-in models.
6. Build caller personas by accent, mood, behavior, interruptions, noise, network and intent.
7. Simulate real voice interactions through audio and telephony paths.
8. Score calls across audio, transcript and configurable business KPIs.
9. Recommend concrete fixes such as prompt changes and model swaps.
10. Backtest recommended fixes against recorded cases.
11. Define personas and scenarios as API-triggered regression tests.
12. Correct grammar and spelling with contextual awareness.
13. Suggest style and tone improvements for readability.
14. Detect errors in real time and give instant feedback.
15. Connect to common writing platforms.
16. Keep the interface usable at all skill levels.
17. Capture corrections and named-owner approval before consequential use.
18. Export versioned reviewer-approved interaction evidence linked to test cases with source references and unresolved questions.

Everything these tools do, in one app

• Automated data processing Automatically processes data with customizable workflows.Found in Hamming AI (YC S24)
• Real-time analytics dashboard Provides an intuitive dashboard for real-time analytics and reporting.Found in Hamming AI (YC S24)
• Business software integration Integrates with popular business software.Found in Hamming AI (YC S24)
• Scalable architecture Supports growing data needs with a scalable architecture.Found in Hamming AI (YC S24)
• Predictive insights Uses built-in machine learning models to provide predictive insights.Found in Hamming AI (YC S24)
• Caller persona builder Allows defining callers by accent, mood, behavior, interruptions, background noise, network conditions, and intent.Found in NovaSynth by Noveum
• Real voice simulation Simulates real voice interactions through actual audio and telephony paths.Found in NovaSynth by Noveum
• Multi-dimensional scoring Evaluates calls across 30+ audio scorers and 100+ transcript scorers, plus configurable business KPIs.Found in NovaSynth by Noveum
• Fix recommendations Suggests concrete changes like system prompt adjustments and model swaps, and allows backtesting fixes.Found in NovaSynth by Noveum
• API-based regression testing Enables defining personas and scenarios as tests triggered via API for automated regression testing.Found in NovaSynth by Noveum
• Grammar and spelling correction Automatically corrects grammar and spelling with contextual awareness.Found in fixa
• Style and tone suggestions Provides style and tone suggestions to enhance readability.Found in fixa
• Real-time error detection Detects errors in real time and gives instant feedback.Found in fixa
• Writing platform integration Offers integration options with popular writing platforms.Found in fixa
• User-friendly interface Provides an interface suitable for all skill levels.Found in Hamming AI (YC S24), fixa

What goes in, what comes out

The workflow

1. In
   Start with

   Call personas, simulated voice interactions, transcript scorers, writing corrections and fix recommendations

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect call personas

4. 3

   Simulated voice interactions

5. 4

   Transcript scorers

6. 5

   Writing corrections and fix recommendations

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewer-approved interaction evidence linked to test cases

AI does the heavy lifting, people stay in charge

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 test scenario set and licensed voice paths; final quality and release checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Test case and persona setup, Editable interaction review, Client proof and delivery. Use a thumbnail gallery for test runs, a large central review canvas, and a right-hand panel for scorers, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant interaction. Make the task-specific outcome reviewer-approved interaction evidence linked to test cases 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

Buyer-owned test cases, authorized interaction recordings and permitted research sources. Cloud asset storage, test-file import/export and release 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

   6 days

   One buyer segment, one recurring use case; first modules: process interaction data with customizable workflows; show real-time analytics and reporting in a dashboard. 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 AI product teams and QA engineers testing and improving AI systems and text use it to solve "AI systems and text are tested in scattered tools, so interaction evidence, error corrections and fix decisions are not kept in one reviewable place"?
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 test cases per review hour and corrections after release.
4. Measure, then decide. Track accepted test cases per review hour and corrections after release; 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 test scenario set and licensed voice paths; final quality and release checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: process interaction data with customizable workflows; show real-time analytics and reporting in a dashboard. Support the third module with operator review: integrate with common business software. 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 interaction evidence linked to test cases. Retain the explicit scope boundary: One fixed test scenario set and licensed voice paths; final quality and release checks remain human.

What the build depends on. Asset upload and preview, asynchronous simulation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed test scenario set and licensed voice paths; final quality and release checks 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 interaction package. Offer a monthly production allowance after repeat demand. Quote complex voice, telephony or specialist QA separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved interaction evidence linked to test cases. 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 review cycles while keeping every AI interaction correction traceable. Demonstrate a concrete reviewer-approved interaction evidence linked to test cases using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

AI product teams and QA engineers testing and improving AI systems and text 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 interaction evidence linked to test cases from a small authorized input set, with a transparent calculation of accepted test cases per review hour and corrections after release and no promised savings.

The first 30 days

1. Week 1: interview five AI product teams and QA engineers testing and improving AI systems and text and inspect a recent example of AI systems and text are tested in scattered tools, so interaction evidence, error corrections and fix decisions are not kept in one reviewable place.
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 test cases per review hour and corrections after release, 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 test cases per review hour and corrections after release. 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 test cases per review hour and corrections after release; 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 interaction evidence linked to test cases. 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 test scenarios, scorer configurations and review examples, together with reliable delivery for a narrow AI QA niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for AI product teams and QA engineers testing and improving AI systems and text. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Hamming AI (YC S24), NovaSynth by Noveum, fixa, manual QA scripts and generic test tools. Compare this product with the buyer's present method on accepted test cases per review hour and corrections after release. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Simulation attempts, voice or telephony processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved interaction evidence linked to test cases. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Reviewers approve substantive changes and release scope. One fixed test scenario set and licensed voice paths; final quality and release checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.