Industries / Insurance

AI for insurance, on one connected platform.

Policy documents, claims records and correspondence connected into one workflow across underwriting and policy operations, with an audit trail on every change.

Astra Short on time? Ask about this page in plain words.

What we built for insurance. A full-stack AI platform for an insurance broker.

Connected data

Structured and unstructured insurance data brought into one system, replacing siloed tools.

Underwriting and policy workflows

Faster, structured workflows across underwriting and policy operations.

Access and audit trail

Role-based access, with an audit trail on every record change.

What else we can build for insurance. Capabilities we offer. We have not delivered these in this industry yet.

Claims intake

Claims documents read and structured, with exceptions routed to handlers.

Submission triage

Incoming submissions classified and prioritised for underwriters.

Policy document checks

Policy wording compared against templates, with differences flagged.

Customer and broker assistants

Answers to routine policy questions, with a handover to staff.

Fraud flags for review

Unusual claims flagged with the reasons, for investigators to review and decide.

Quote and renewal support

Submission data pulled into one file, so quotes and renewals start from a complete picture.

Broker and agency tools

Policy, commission and correspondence data in one view for agents.

Regulatory reporting

Evidence gathered and reports drafted for your compliance team, with attention to NAIC requirements.

Match the autonomy to the task. Routine data work can run with supervision. Decisions stay with people.

  1. L1WorkflowA fixed, auditable path.
  2. L2AssistantAI drafts. A person reviews.
  3. L3Supervised agentAI acts and flags exceptions.
  4. L4AutonomousRoutine, low-risk, monitored.
  5. Human-ledPeople decideAI supports the analysis.
  • Data ingestion and matching

    A fixed, auditable workflow, with AI reading the documents.

    L1 Workflow
  • Claims processing

    A governed workflow, with an agent flagging exceptions.

    L1 WorkflowL3 Supervised agent
  • Underwriting and settlement decisions

    Stay with people. AI prepares the information.

    Human-led

Is insurance data spread across tools that do not talk to each other?

The technology behind our insurance platform. Built around connecting policy, claims and correspondence data, with access control on every record.

01 / 05

Models

The language models behind each feature, picked per task on cost, speed and quality.

  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • LangChain

From scattered policy data to one workflow. Four stages, with your operations team reviewing a working version at each one.

  1. Discovery

    Map the current workflow, the systems and data involved, and what good looks like.

    Discovery notes

    • The systems and documents involved
    • Where reconciliation happens today
    • Access and audit rules
  2. Scope & architecture

    Agree how policy, claims and correspondence data connect, and who can see and change each record.

    Solution design

    • Platform architecture
    • Access and audit design
    • Success measures
  3. Build

    Build on real policy and claims data, with your operations team reviewing each milestone.

    Working build

    • Working platform on real data
    • Team review
    • Quality checks
  4. Ship & support

    Roll out with role-based access, an audit trail on record changes, monitoring and a defined support window.

    Production rollout

    • Production rollout
    • Monitoring
    • Support window

Why Avinya for insurance. We have connected insurance data in production.

Delivered for a broker

A connected platform for an insurance broker, with about 2.5x faster processing.

Decisions stay with people

Underwriting and settlement decisions stay with your team. The platform prepares the information.

Every change tracked

Role-based access, with an audit trail on every record change.

Support after go-live

Data quality, access rules and workflows are tuned after launch, with a defined support window.

Engagement models. Pick the shape that fits where you are.

Ongoing

Dedicated team

A team that keeps extending the platform as products, lines of business and systems change.

Fixed scope

Project-based

A defined scope for one workflow, such as claims intake or submission triage, with milestones and a delivery date.

Advisory

Consulting & advisory

Advice on data, access and audit design before you commit to a build.

What our clients say.

AI platform for an insurance broker

“Avinya Labs built our full-stack AI solution … Their expertise accelerated our launch and significantly reduced execution risk.”
SandraProject lead, Insurance broker
~2.5xFaster processing speed
Read the case study →

AI for insurance FAQs.

What did you build for an insurance broker?

A full-stack AI platform that connects policy documents, claims records and correspondence into one workflow, with about 2.5x faster processing.

Is every change tracked?

Yes. The platform has role-based access and an audit trail on every record change.

Does AI make underwriting decisions?

No. It connects and prepares the information. Underwriting and settlement decisions stay with people.

How is AI used in insurance?

Mainly to take the reading and data entry out of operations: claims intake, submission triage for underwriters, policy wording checks, connecting data across systems, and assistants for routine policy questions. Underwriting and settlement decisions stay with people.

Where should an insurer or broker start?

With one workflow where the documents already exist, such as claims intake or submission triage. We map the data and the handoffs, agree what a good result looks like, and build a working version your team can review.

How is policyholder data protected?

Access is scoped by role and every record change is logged. Personal data handling, where data is stored and which models can see it are agreed with you during discovery.

Are you familiar with insurance standards?

Yes. We are familiar with ACORD data standards and NAIC requirements, and we design the platform around the ones that apply to you. Your compliance team confirms what applies.

How long does an insurance AI project take?

A prototype on a sample of your policy and claims data takes about 6 to 9 weeks, and a first version your team can use about 8 to 12 weeks. Production, with access rules and an audit trail, typically takes 3 to 5 months.

Can we sign an NDA first?

Policy and claims data is sensitive. Yes. We can sign an NDA before you share anything, and we agree ownership of the work in the contract before the work starts.

How does AI for insurance keep decisions explainable?

Every suggestion shows the policy text or claim record it came from, and people make the final call. AI for insurance uses the NIST AI Risk Management Framework to document risk.

Free tool

How ready is your business for AI?

Twenty questions, about four minutes. You get a score across five areas and what to fix first.

Take the free check

Tell us what's slowing the team down.

Or book a call · contact@avinyalabs.co

Tell us about your project

Rather talk it through? Book a call · or email contact@avinyalabs.co

What are you building?

Budget (optional)

0 / 2000
Best way to reach you

Loading available times

Ask Astra

Answers come only from our services, case studies and blog, with sources you can check.

Try asking

AI can make mistakes. Check the linked sources. Book a callTalk to the team