LiLa, Insurance Vertical AI

Meet LiLa, our Insurance Vertical AI which empowers carriers to spend less time on manual work and more time underwriting, adjusting, and writing business.

From FNOL to a claim ready to work

Adjuster

From FNOL to a claim ready to work

Adjuster

From submission to quote-ready in one pass

Underwriter

From submission to quote-ready in one pass

Underwriter

From scattered files to one case summary

Litigation Adjuster

From scattered files to one case summary

Litigation Adjuster

From legacy risk to one governed view

CTO

From legacy risk to one governed view

CTO

Find Exposed Fields Before a Breach

CISO

Find Exposed Fields Before a Breach

CISO

The Problem

Generic AI doesn't understand insurance data

Underwriting

Generic models can't reliably extract COPE data, parse loss runs, or read a statement of values. Every submission still needs a human to pull the numbers out by hand.

Underwriting

Generic models can't reliably extract COPE data, parse loss runs, or read a statement of values. Every submission still needs a human to pull the numbers out by hand.

Claims

A generic LLM doesn't know what belongs in a claim file or how to keep an audit trail intact. FNOL intake and claims classification still run through manual review.

Claims

A generic LLM doesn't know what belongs in a claim file or how to keep an audit trail intact. FNOL intake and claims classification still run through manual review.

Claims

A generic LLM lacks insurance-specific claims knowledge and audit controls, while FNOL intake and claims classification still require manual review, creating delays, inconsistencies, and operational risk.

Litigation risk

Litigation risk depends on jurisdiction, attorney behavior and injury severity. Generic models summarize claims but cannot reliably predict which cases reach court.

Litigation risk

Litigation risk depends on jurisdiction, attorney behavior and injury severity. Generic models summarize claims but cannot reliably predict which cases reach court.

Data and governance

Generic AI has no built-in governance for high-risk models or third-party data sources. Every new feed becomes a manual compliance review before it can be trusted.

Data and governance

Generic AI has no built-in governance for high-risk models or third-party data sources. Every new feed becomes a manual compliance review before it can be trusted.

The Solution

Our insurance-trained AI removes the manual work

Underwriting

LiLa extracts COPE data, loss runs, and statement of values automatically, turning submission triage from a manual read into a structured intake.

Claims

LiLa reads FNOL documents and claim files the way an adjuster does, keeping the audit trail intact from intake through classification.

Litigation risk mitigation

Generic AI can't predict which claims are headed to court, it can only summarize the file in front of it.

Data and governance

LiLa runs inside the insurer's own environment, so third-party data and high-risk models stay governed from the moment they're connected.

Customers

We Work With

Leading Insurers

We've helped insurers accelerate data workflows, improve accuracy, and modernize operations

Our Customers

The successful migration to the Guidewire ClaimCenter Cloud is a testament to our commitment to leveraging modern technology for enhanced operational efficiency and customer satisfaction. Golden Bear is proud of our collaboration with InsOps, whose expertise and strategic approach were instrumental in achieving this transformation.

Michael Hall

Vice President

Golden Bear Insurance Company