Get Clean, High-quality Insurance Data Faster

LiLaGPT, Our insurance-trained AI Small Language Model integrates with existing systems and works with structured and unstructured data, so insurers get reliable, usable data without replacing existing platforms.

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Reduces Manual Work

LiLaGPT interprets insurance data consistently across policy, claims, billing, and partner systems. Teams no longer need to manually decode field meanings or reconcile how the same data is represented in different systems.

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Decreases Time Spent Preparing Data

LiLaGPT works with fragmented, imperfect insurance data without requiring extensive upfront cleanup. Teams can use data sooner instead of spending weeks standardizing and reshaping it before it becomes usable.

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Faster Time to Value

LiLaGPT works alongside existing systems and workflows, without requiring replacement or reconfiguration. This reduces integration effort and lets insurers start using reliable data without interrupting business operations.

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Why This Matters

Poor data quality slows decisions and limits AI adoption

Insurance data is scattered across policy, claims, billing, and partner systems. Each platform stores information differently, creating inconsistencies that slow operations and limit visibility. Manual consolidation cannot keep up with this complexity and often introduces new errors.

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Insurance Data is Not Ready for Use

Insurance data exists across policy, claims, billing, and partner systems, but it is not in a usable form. Teams must reconcile formats, meanings, and gaps before data can support reporting or analysis.

Stage 1

Insurance Data is Not Ready for Use

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Project Roadmap

Insurance Data is Not Ready for Use

Automation Breaks on Poor Data

Insurers want to automate processes to improve data quality and usability. But most of them run legacy systems that produce fragmented, context-poor data that limits reliable automation and AI.

Stage 2

Automation Breaks on Poor Data

Insurers want to automate processes to improve data quality and usability. But most of them run legacy systems that produce fragmented, context-poor data that limits reliable automation and AI.

Automation Breaks on Poor Data

Insurers want to automate processes to improve data quality and usability. But most of them run legacy systems that produce fragmented, context-poor data that limits reliable automation and AI.

Generic LLMs Have Limits

Insurance data is often fragmented and without insurance domain knowledge, Generic AI models struggle to interpret fields, relationships, and rules accurately, limiting the value they can deliver in real-world insurance environments.

Generic LLMs Have Limits

The Solution

LiLaGPT, an Insurance-trained Small Language Model that Makes Insurance Data Usable at Scale

Our SLM understands insurance data across systems and formats, bringing context, consistency, and reliability to data without replacing existing platforms or requiring upfront cleanup.

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How does InsOps Help?

How Insurers Turn Fragmented Data into Usable Data

InsOps addresses the core problem behind poor data quality: lack of context across systems. Instead of forcing cleanup projects or platform changes, LiLaGPT, our insurance-trained SLM interprets data where it lives, applies insurance context, and makes it usable across the business.

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Understands Insurance Data

LiLaGPT recognizes when different systems describe the same policy, claim, or customer, even when fields or formats differ. This reduces the need for manual mapping and reconciliation across systems.

Understands Insurance Data

LiLaGPT recognizes when different systems describe the same policy, claim, or customer, even when fields or formats differ. This reduces the need for manual mapping and reconciliation across systems.

Works with Messy Data

LiLaGPT does not require clean or perfectly structured inputs to be effective. It works with fragmented and mixed-format insurance data, reducing the need for upfront cleanup before data can be used.

Works with Messy Data

LiLaGPT does not require clean or perfectly structured inputs to be effective. It works with fragmented and mixed-format insurance data, reducing the need for upfront cleanup before data can be used.

Integrates with Existing Systems

LiLaGPT works alongside existing systems and workflows without requiring platform replacement or reconfiguration. Insurers can improve data reliability and usability across systems while keeping operations stable and avoiding new platform risk.

Integrates with Existing Systems

Testimonial

What Our Insurance Partners Say

Hear how insurers accelerated data workflows, improved accuracy, and modernized operations with InsOps.

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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

FAQ

Frequently

Asked Questions

Have questions? Our FAQ section has you covered with
quick answers to the most common inquiries.

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What does InsOps SLM-as-a-Service actually do?

How is this different from using a generic LLM with our data?

Does the SLM require clean or standardized data to work?

Do we need to replace or reconfigure our existing systems?

What does InsOps SLM-as-a-Service actually do?

How is this different from using a generic LLM with our data?

Does the SLM require clean or standardized data to work?

Do we need to replace or reconfigure our existing systems?

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Every Insurer We Work With

Moves Faster and Operates Smarter.

Join us and turn complex data challenges into streamlined, automated operations with the help of our Insurance-trained SLM

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Join Us Now

Each Project, Our

Design is Great.

Join us and turn complex data challenges into streamlined, automated operations with the help of our Insurance-trained SLM

Book an Appointment