[ INDUSTRIES · Insurance ]

Insurance

Insurance is a document business wearing a risk business's clothes. We put document AI, predictive models, and agents on the paper-heavy middle — submissions, claims, subrogation, and servicing — with adjusters and underwriters on every consequential call.

Enterprise-gradeDeploys in your cloudYou own the IP
The landscape

Carriers move enormous volumes of unstructured paper — ACORD forms, medical records, police reports, broker submissions — through processes that are still largely manual. Each hand-off adds cycle time, leakage, and inconsistency, and the best people spend their day assembling files instead of assessing risk.

AI changes the economics by reading, extracting, and assembling that paper with provenance, and by scoring severity and recovery early. But claims and underwriting are regulated, consequential decisions — so we design for straight-through where it's safe and human review where it isn't, with a full audit trail either way.

What's forcing the change

  • Combined-ratio pressure and rising loss-adjustment expense
  • Unstructured documents at the center of every workflow
  • Leakage in claims handling and missed subrogation recovery
  • Talent scarcity in underwriting and claims
Where we build

AI solutions for Insurance.

Claims triage & FNOL

Agents intake first notice of loss, assemble the claim file, estimate severity, and route complex cases to the right adjuster.

Doc AI / OCRRAGSeverity ML
65% straight-through intake

Underwriting submission intake

Submission agents extract, enrich, and pre-score broker submissions so underwriters open a decision-ready file.

VLMSchema extractionAgents
3× submissions per underwriter

Subrogation & leakage

Closed-claim reviews that detect missed recovery, explain why, and draft the demand package for a specialist.

Predictive MLDoc AILLM drafting
recovered leakage, found not lost

Policy servicing

Endorsements, renewals, and document requests handled end to end, with approval gates on binding actions.

AgentsRules engineMCP
faster turnaround, full audit trail

Fraud detection

Anomaly and network models surface suspicious claims with explanations for SIU review.

Graph featuresGradient boostingSHAP
more flags that hold up
On the platform

Document AI, predictive models, and agents share one platform — grounded in your policy and claims data, governed by evaluation and audit logging, and deployed inside your environment.

Typical outcomes

  • Cycle times cut on the paper-heavy middle
  • Leakage recovered and severity caught earlier
  • Adjusters and underwriters focused on risk, not assembly
Compliance & governance

Every system is architected to your regulatory surface, with controls and audit trails built in.

NAIC model rulesState DOI requirementsFair claims handlingHIPAA (health lines)SOC 2Audit trails
Evidence

Related success stories.

Insurance

Finding the money left in closed claims

leakage
found, not lost
closed claims
re-scored at scale
Read the story
Healthcare

Prior authorization in minutes

hrs → min
packet assembly
100%
audit trail
Read the story
FAQ

Questions from Insurance teams.

How do you decide straight-through vs human review?
We map which decisions are safe to automate end-to-end and which require an adjuster or underwriter, then gate accordingly. Low-risk, high-volume paths run straight through; consequential or ambiguous cases route to a person with the file already assembled.
Can it read our messy documents?
Yes — vision-language models plus OCR handle scanned, photographed, and mixed-format documents, and every extracted field carries provenance back to the source so reviewers can verify.
Get started

Bring us a hypothesis. Leave with a system.

Tell us what's eating your team's time. We'll give you an honest read on whether AI is the right tool — and if it is, a scoped v1 with a timeline and cost.