AI app for it and development · no coding needed
AI interaction evidence review and QA workspace
Reduce review cycles while keeping every AI interaction correction traceable.
Made for: AI product teams and QA engineers testing and improving AI systems and text
What it does for you
The problem
AI systems and text are tested in scattered tools, so interaction evidence, error corrections and fix decisions are not kept in one reviewable place.
What it gives you
Reviewer-approved interaction evidence linked to test cases
What you give it
Call personassimulated voice interactionstranscript scorerswriting correctionsfix recommendations
Build your own version of Hamming AI (YC S24), NovaSynth by Noveum and more
One app with what these 3 AI tools do, yours to keep and change: Hamming AI (YC S24), NovaSynth by Noveum, fixa.
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
How it works, step by step
- Process interaction data with customizable workflows
- Show real-time analytics and reporting in a dashboard
- Integrate with common business software
- Support growing data volume with a scalable architecture
- Provide predictive insights from built-in models
- Build caller personas by accent, mood, behavior, interruptions, noise, network and intent
- Simulate real voice interactions through audio and telephony paths
- Score calls across audio, transcript and configurable business KPIs
- Recommend concrete fixes such as prompt changes and model swaps
- Backtest recommended fixes against recorded cases
- Define personas and scenarios as API-triggered regression tests
- Correct grammar and spelling with contextual awareness
- Suggest style and tone improvements for readability
- Detect errors in real time and give instant feedback
- Connect to common writing platforms
- Keep the interface usable at all skill levels
- Capture corrections and named-owner approval before consequential use
- Export versioned reviewer-approved interaction evidence linked to test cases 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 AI interaction evidence review and QA 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 AI interaction evidence review and QA 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 links3 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data196 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 review cycles while keeping every AI interaction correction traceable. For AI product teams and QA engineers testing and improving AI systems and text, convert call personas, simulated voice interactions, transcript scorers, writing corrections and fix recommendations into reviewer-approved interaction evidence linked to test cases. The benefit is a testable hypothesis, measured through accepted test cases per review hour and corrections after release; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect call personas, simulated voice interactions, transcript scorers, writing corrections and fix recommendations, then follow this sequence: 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. Resolve uncertain cases with qualified reviewers, approve reviewer-approved interaction evidence linked to test cases, and measure accepted test cases per review hour and corrections after release against a documented baseline.
How the AI works
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.
Safeguards
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.
What to build first
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.
What it can connect to
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.
The screens in detail
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.
