AI app for insurance · no coding needed
Claims document translation alignment
Bilingual claims evidence with preserved references.
Made for: Specialist insurance claims administrators

What it does for you
The problem
Translated evidence loses dates and document references.
What it gives you
Reviewed bilingual evidence pack
What you give it
Authorized claims documentsapproved terminology
How it works, step by step
- Align source passages
- Translate factual text
- Compare identifiers
- Flag ambiguous terms
- Record reviewer edits
- Export aligned evidence
What you see on screen
- Document pair
- Identifier checks
- Linguist review
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 Claims document translation alignment 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 Claims document translation alignment 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 Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data197 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
For specialist insurance claims administrators, turn authorized claims documents and approved terminology into reviewed bilingual evidence pack. Address this specific problem: translated evidence loses dates and document references. The aim: bilingual claims evidence with preserved references. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.
The buyer creates a project, supplies authorized claims documents and approved terminology, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final reviewed bilingual evidence pack before use. Retain source links and a version history for the next cycle.
How the AI works
Translate with exact references and preserve uncertainty. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.
Safeguards
Separate document preparation from coverage, underwriting and claims decisions. Authorized professionals review policy meaning and customer commitments. One language pair; no claim determination. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.
What to build first
Costed pilot: One language pair; no claim determination. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: align source passages; translate factual text. Support the third task through an assisted review queue: compare identifiers. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of reviewed bilingual evidence pack. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.
What it can connect to
Broker-approved policy documents, case records and carrier requirements. Document formats, subtitle formats, media storage and publishing systems. Confirm language, font and layout support for each requested output. Begin with uploads and exports of authorized claims documents and approved terminology. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
The screens in detail
Use aligned source and target language panes with shared terminology highlights. Include a language status grid, reviewer assignment queue, and layout or timeline preview. Show unresolved ambiguities next to the affected passage. Keep approvals separate for each language and version. Open with document pair; move into identifier checks for the detailed task; finish in linguist review for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.





