Industry · Insurance
Claims and policy wording are a retrieval problem.
Insurance runs on documents that contradict each other across versions and territories. That's precisely where grounded retrieval earns its keep — and where an ungrounded model is a mis-selling risk.
- Generative AI & RAG
- AI Agents
- Strategy & Governance
Why now
Claims volume is seasonal and staffing isn't, so cycle time collapses exactly when customers are least patient. Meanwhile the wording that decides a claim sits across policy documents, endorsements and years of amendments. The teams making progress use AI to extract, retrieve and draft — and keep the coverage decision with a human whose reasoning is on the record.
The problem
Where insurance teams lose time today.
- 01
Claims intake is manual extraction
Photos, invoices, police reports and hospital bills arrive as scans. Someone keys them into a form before the claim can even be triaged.
- 02
Coverage answers are buried in endorsements
The base wording says one thing, the endorsement says another, and the version that applies depends on the inception date.
- 03
Surge periods break the SLA
A storm or a policy-renewal wave triples intake overnight and cycle time is the first thing to go.
Constraints
What makes this harder than a generic chatbot.
- 01
The coverage decision stays with a human
The system extracts, retrieves the applicable wording and drafts the reasoning. An adjuster confirms or overrides, and their reasoning is recorded — which is what a complaints process and an ombudsman will ask for.
- 02
Version-aware retrieval or nothing
Every retrieved clause carries its document version and effective date, and queries are scoped by policy inception. Quoting the wrong revision is a regulatory problem, not a UX one.
- 03
Extraction accuracy is reported per document type
A typed invoice and a handwritten claim form are different problems. We publish accuracy per type before you decide what to automate.
Integrations
Systems we wire into.
- Guidewire & Duck Creek
- Policy administration systems
- OCR and layout-aware extraction
- Salesforce & Zendesk
- Twilio & WhatsApp Business
- Snowflake
- Document management systems
Services
What we build for insurance.
- RAG for insurance policy wordingRetrieval across policy wording, endorsements and claims manuals, scoped by product, territory and effective date — every answer carrying its citation.Read
- AI agents for claims processingAgents that extract from claim documents, chase what's missing, and hand the adjuster a complete file with the coverage reasoning already drafted.Read
- AI governance for insurersRisk classification, model documentation and oversight design for insurance use cases — built around what a complaint, an audit or an ombudsman review will ask for.Read
How an engagement is judged
- A scope small enough to finish, agreed in writing in week one
- An eval suite built from your own data, versioned in your repo
- Weekly numbers on the metrics we agreed, including the bad ones
- Code, IP and repo yours from day one — no proprietary runtime of ours
We publish no per-industry result metrics because almost all client work is under NDA and we won't quote numbers we can't evidence. What we can show is the process and the in-house work on /work, which is public and running.
Pricing ladder
Three ways to start.
Sprint validates · Build productionizes · Retainer scales. The Sprint fee credits toward Build.
Step 1 · Validate
30-day Sprint
Prove the use case before you commit. Working prototype on real data, eval scores, and an honest signal in 30 days. Fixed scope, fixed fee.
$4,500 fixed
Learn more- Most teams land here
Step 2 · Build
Prototype → Production
Turn the validated prototype into a real product. Auth, DB, payments, tests, monitoring, deployed. Sprint fee credits toward this engagement.
from $6,000
Learn more Step 3 · Scale
Managed Retainer
Ongoing operation, eval cycles, model iteration, and cost guards. We keep the system improving so your team can focus on growth.
from $750/mo
Learn more
FAQ
AI in insurance: the questions we get.
Can AI decide a claim?
It shouldn't, and we won't build it that way. It can assemble the evidence, retrieve the applicable clause and draft the reasoning for an adjuster — which removes most of the elapsed time without removing the accountable decision.
Our documents are decades of scanned PDFs. Is that workable?
Yes, and it's the normal starting point. Expect the first phase to be extraction quality rather than answering — we report per-document-type accuracy so you know what's safe to build on before anything is automated.
How do you handle regional wording differences?
Territory and effective date are retrieval filters, not prompt suggestions. A query for a Maharashtra policy written in 2023 cannot return a 2026 national endorsement.
Contact
Talk to us about AI in insurance.
Tell us where you are now and where you want to be. We reply within one business day.
Or skip the form — book a Calendly slot directlyadmin@neuroxai.com · +91 70149 99768
Remote-first team across India · US · EU · HQ in Udaipur, India
Other industries
Similar constraints, different vertical.
- AI for Fintech & BankingMost banking AI pilots die in review, not in testing. We build the boring parts first — decision logs, evals, human approval gates — so the model has somewhere safe to be useful.Read
- AI for HealthcareHealthcare AI earns trust by being narrow. We build systems that retrieve, draft and route — and leave every clinical judgement with a person who signs it.Read
- AI for Retail & E-commerceA bot that says 'let me check that for you' and can't is worse than no bot. We connect agents to the order, the inventory and the returns policy so they can finish the job.Read