AI Agents for Insurance
A chatbot tells a policyholder how to file a claim. An AI agent collects the FNOL data, validates it against the policy record, creates the claim in your system, assigns it to the right adjuster, and sends the first status message, all from a single inbound contact. The difference matters in insurance because half-finished workflows create the cost: when a FNOL lands by email and a handler has to open three systems and re-key the data, that's a workflow problem, not a training problem. We build insurance AI agents with a defined scope per workflow, explicit escalation rules, and compliance-aware data handling.
FNOL intake agents that collect, validate, and route first notice of loss from any channel
Underwriting data agents that query CLUE, MVR, credit bureaus, and property databases and return structured output for review
Claims status agents that give policyholders real-time updates without advisor involvement
Compliance-aware architecture with full audit trails and configurable human-in-the-loop checkpoints
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Your claims team re-keying the same FNOL data from emails, portals, and phone notes into your system of record, every single intake?
Underwriting data collection from CLUE, MVR, and third-party sources taking days and pulling attention away from actual risk assessment?
Plain answer
RaftLabs builds AI agents for insurance workflows: FNOL intake, underwriting data extraction, claims status, renewal reminders, and fraud-signal flagging. Unlike rule-based RPA, these agents act end-to-end across systems, apply configurable decision logic, and escalate to human reviewers at defined checkpoints.
What to remember
Proof
An agent fixes the half-finished-workflow problem by owning it end-to-end. It escalates the exceptions that genuinely need a human. Each agent runs one insurance workflow well. We scope the integration with your policy administration system and claims platform during discovery, where most projects find their real complexity.
McKinsey puts 30 to 40% of an underwriter's time on administrative work like rekeying data and running manual analyses ("Insurance productivity 2030"). That is the work an agent should take off your team.
We are direct about proof. RaftLabs has shipped AI agents and document data-extraction systems, not yet a live insurance claims or underwriting agent. The engineering is the same one domain over. The adjacent work below shows it.
Capabilities
Extracts structured FNOL data from email, portal, or phone transcript, validates against the policy record, creates the claim, and routes it to the right adjuster queue.
Runs parallel CLUE, MVR, credit, and property database retrieval, assembling a structured underwriting package the moment all sources return.
Answers policyholder status enquiries in plain language from live claims data, and sends proactive updates at defined workflow trigger events.
Drives personalised, compliance-checked renewal outreach across the pre-renewal window, escalating coverage questions to a human.
Aggregates configurable fraud indicators into a transparent risk score, routing high-risk claims to SIU with the specific signals that triggered the flag.
Reviews claim narratives at defined lifecycle points to flag recovery-worthy cases for professional review, never determining liability itself.
How we work
We map your target workflow, systems, and where your team is losing time. You leave the first phase with a written scope document and a fixed-price quote.
System integrations, decision logic, and human-in-the-loop checkpoints designed against your compliance requirements.
The agent workflow built as a directed graph with explicit state transitions, tested against real claim and submission scenarios.
Claims and underwriting teams trained on the escalation workflow before the v1 goes live. We extend to more workflows from there.
Proof
We scope the work, calculate the cost, and lock it in writing before any development starts.
We have built AI agents and document data-extraction systems across fintech, healthcare, logistics, and retail.
The scoping document defines the workflow state machine, integration points, and escalation logic before any code is written.
A data flow diagram, controls inventory, and audit trail specification are delivered alongside the working system.
Proof
An offline-first AI-OCR pipeline that reads receipts and POS documents, then syncs structured records across sites. This is the FNOL-neighbouring problem: turn messy inbound documents into clean, structured data.
Document data extraction
An AI agent that conducts interviews and returns structured output, built on Anthropic Claude via AWS Bedrock. Same agent-engineering pattern behind an insurance workflow agent.
AI agent build
A French-first school management platform and LMS for K-12 schools in West Africa. Attendance, marks, fees, and role-based portals for administrators, teachers, students, and parents, built by a six-person RaftLabs team over about six to seven months.
Work with us
Tell us the workflow you want to automate, the systems you run on, and where your team is losing time to work an agent should handle. We'll scope what it takes and give you a fixed cost.
RPA follows fixed rules on structured data and breaks when formats change. AI agents handle unstructured variation because the LLM reads intent and content, not just field positions. Agents can also take multi-step actions across systems: validate a policy number, create a claim record, route it to the right adjuster, and send acknowledgement as one end-to-end workflow with the full state tracked and auditable.
Every agent action is logged: the data it retrieved, the decision it made, what it wrote to the system of record, and when. We confirm data processing agreements are in place before any policyholder data reaches an LLM. For any output that can decline, delay, or price a policyholder, the agent surfaces the specific reasons and routes to a human for review.
We integrate with systems that expose REST APIs or SOAP web services, including Guidewire PolicyCenter and ClaimCenter, Duck Creek Policy and Claims, and Applied Epic. Bureau data including CLUE and MVR is accessed via LexisNexis, ISO, or state-specific aggregators. Integration complexity is the single biggest variable in project scope, so we confirm API access and sandbox environments during discovery.
Cost follows scope: a focused insurance AI agent covering one workflow, one system integration, defined escalation logic, and compliance-aware architecture is a different build than a multi-agent build covering FNOL intake, underwriting data extraction, and claims status with integrations to a policy admin system and claims platform. After scoping the workflow and integrations, you get a fixed written quote.
Buy the system of record, build the process layer. Pain in policy admin or rating means evaluating platforms. Pain in intake, triage, document chaos, legacy handoffs, or work between systems means custom fits better. Traditional underwriting software is a system of record; agentic AI is the working layer on top that handles broker emails, ACORD forms, loss runs, and manual handoffs the platforms leave to people. Most insurers need both. Start with a core platform for the main workflow, and add specialized point tools as gaps appear.
What should stay human: complex coverage decisions with real financial consequences, sensitive claims conversations after a loss, final underwriting judgment calls, any interaction where trust-based advising matters more than speed. Support the advisory role, do not replace it. Low-complexity, high-volume policies: straight-through within thresholds. Complex commercial and specialty: agents support, underwriter decides. Extract, enrich, appetite-screen, risk-score, prepare the file, then the human makes the call. The real implementation work is defining escalation rules and success metrics with underwriting and claims leadership, not the model.
Yes. For email, the agent monitors a designated inbox and runs extraction against the email body and attachments. For web portal submissions, it receives a webhook or polls the portal API. For phone transcripts, it extracts FNOL fields from unstructured conversational text. Each channel feeds the same downstream validation and routing workflow, producing a consistent structured claim file.
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