Agentic AI for carriers, MGAs, and brokers
Underwriting, submission intake, and claims still move at the speed of a human reading PDFs. Agents do the reading, cross-checking, and file assembly — inside your filed rules and under licensed review.
- Submission & FNOL intake
- Underwriting evidence assembly
- Claims triage with adjuster sign-off
- Jurisdiction-aware compliance
Insurance runs on regulated paperwork
Every quote and claim is a chain of documents that has to satisfy a filed rate, a state acknowledgment clock, and unfair-claims-practices law.
A commercial submission arrives as a broker email, a 40-page ACORD packet, loss runs in three different formats, and a SOV spreadsheet. An underwriter spends most of the day reconciling those into something a rating model can consume — not exercising judgment, just moving and checking data.
Claims are worse. The clock starts at first notice of loss, and states like California and New York impose hard deadlines on acknowledgment, investigation, and payment. Miss them and you're exposed to bad-faith and unfair-claims-practices penalties — regardless of whether the underlying decision was correct.
This is exactly the shape of work agents do well: high-volume, document-heavy, rule-bound, and bottlenecked on retrieval rather than on the licensed decision at the end. We automate the legwork and leave the regulated decision with your people.
Where agents earn their keep
Concrete workflows across the policy lifecycle — each one ships with guardrails and a human checkpoint.
Submission intake & clearance
Parse ACORD forms, loss runs, and SOVs into structured risk data, run clearance and appetite checks, and flag conflicts before an underwriter opens the file.
Underwriting evidence assembly
Pull prior loss history, public records, and third-party data, then hand the underwriter a cited risk summary against your filed guidelines — never the rating decision itself.
FNOL & claims triage
Capture first notice of loss 24/7, classify severity and coverage line, detect fast-track vs. complex, and start the acknowledgment clock with the correct state notices.
Coverage & policy lookup
Read the actual policy form, endorsements, and exclusions, then surface the controlling language to the adjuster with citations rather than a black-box answer.
Fraud & SIU referral
Spot anomaly patterns across claims and route suspect files to the Special Investigations Unit with an evidence packet, not an automated denial.
Policyholder & broker support
Resolve status, endorsement, and certificate-of-insurance requests against systems of record, escalating licensed questions to a human agent.
A claim, end to end
The agent compresses the gather-and-check work; the licensed adjuster keeps the decision.
Intake
FNOL captured across channels, deduplicated, and matched to the active policy. The state acknowledgment clock starts and notices fire.
Assemble
Agent reads the policy form, pulls coverage and exclusions, gathers loss documents, and builds a cited claim file.
Triage
Severity and complexity scored; clean fast-track claims flagged, suspect patterns routed to SIU, the rest queued to the right adjuster.
Decide
A licensed adjuster reviews the assembled file and citations, then approves, reserves, or escalates. Every step is logged.
Built for the DOI examiner, not just the demo
Insurance AI fails its first market-conduct exam when nobody can explain why a decision was made. We design the opposite: agents produce evidence and citations, the filed and actuarially-justified rating model stays untouched, and the licensed human signs.
Jurisdiction rules — acknowledgment timelines, fair-claims-practices obligations, adverse-action and FCRA notices, filed-rate constraints — live in a policy layer keyed to the risk state. The same workflow behaves correctly in all 50 states without a fork per jurisdiction.
- No auto-issued denials or adverse underwriting decisions
- State-keyed notice timelines and unfair-practices rules
- Full decision lineage for market-conduct exams
- PII/PHI kept inside your perimeter
A scoring black box vs. an evidence agent
The difference between a model that decides and an agent that does the legwork.
| A scoring black box | An Automatic.co agent | |
|---|---|---|
| Output | A score nobody can explain | A cited file a human can defend |
| Rating | Opaque, unfiled factors | Untouched filed rating model |
| Decision | Auto-denies the claim | Routes to a licensed adjuster |
| Exam readiness | No lineage to show | Full step-by-step audit trail |
| Jurisdiction | One rule for everywhere | State-keyed notices and timelines |
Frequently asked questions
How do you handle state-by-state regulatory variation?
Agents are jurisdiction-aware. Rate, form, and notice rules are encoded as a policy layer keyed to the risk state, so a Texas claim and a New York claim follow different acknowledgment timelines, fair-claims requirements, and filed-rate constraints without separate workflows.
Will an agent make a coverage or claims decision on its own?
No coverage denial, reservation of rights, or adverse underwriting decision is auto-issued. Agents assemble the file, cite the policy language and facts, and route to a licensed adjuster or underwriter for sign-off. The human decides; the agent does the legwork and records the lineage.
What about model bias and unfair discrimination laws?
We separate the rating math (which stays in your filed, actuarially-justified models) from the agent's job, which is gathering and structuring evidence. Every input the agent surfaces is logged, so you can demonstrate to a DOI examiner that decisions rest on permitted, non-proxy factors.
Can agents run inside our own environment for PII and PHI?
Yes. Deployments run in your VPC, on-prem, or air-gapped, so policyholder PII, medical records on health and disability claims, and FNOL data never leave your perimeter. Access is scoped and every retrieval is auditable.
Pick one line. We'll map the workflow.
One working session on your highest-volume underwriting or claims bottleneck — and the compliant path to automating it.