AI app for insurance · no coding needed
Insurance product rating-rule test factory
Catch pricing implementation errors before production.
Made for: Insurer product implementation teams
What it does for you
The problem
Approved rating rules are implemented inconsistently across systems.
What it gives you
Actuary-and-engineer-reviewed test evidence
What you give it
Actuary-approved rulessynthetic test cases
How it works, step by step
- Generate boundary cases
- Compare deterministic rating outputs
- Explain implementation discrepancies
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned actuary-and-engineer-reviewed test evidence 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 Insurance product rating-rule test factory 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 Insurance product rating-rule test factory 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 links1 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 criteria13 KB
- demo/index.htmlThe working demo on sample data200 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
Catch pricing implementation errors before production
Confirm the buyer's problem and scope, collect actuary-approved rules and synthetic test cases, then follow this sequence: 1. Generate boundary cases. 2. Compare deterministic rating outputs. 3. Explain implementation discrepancies. Resolve uncertain cases with qualified reviewers, approve actuary-and-engineer-reviewed test evidence, and measure verified incorrect quotes prevented and QA hours saved minus validation cost 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. Sandbox testing only; no rate recommendations or underwriting decisions. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Separate document preparation from coverage, underwriting and claims decisions. Authorized professionals review policy meaning and customer commitments. Sandbox testing only; no rate recommendations or underwriting decisions. 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: Sandbox testing only; no rate recommendations or underwriting decisions. Implement one approved input format, a bounded representative case set and the first two task modules: generate boundary cases; compare deterministic rating outputs. Support the third module with operator review: explain implementation discrepancies. 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
Broker-approved policy documents, case records and carrier requirements. Approved repositories, application APIs, execution platforms and monitoring systems. Validate current API access and behavior during discovery before promising compatibility. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Authorized input and test setup, Proposed implementation, Test results and release review. Show a work backlog, proposed changes and verification results. Link each item to its source configuration, code or data mapping. Provide execution logs and an owner-facing health view. Keep environments and approval states clearly separated so a draft cannot be mistaken for a live change. Make the task-specific outcome actuary-and-engineer-reviewed test evidence visible beside its evidence, review state and value baseline.
