Get Clean, High-quality Insurance Data Faster
Get Clean, High-quality Insurance Data Faster
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.
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.
Effortlessly connect with your favorite tools. Whether it's your CRM, email marketing platform.
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.
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
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.
Comprehensive Consultation
Project Roadmap
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.
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.
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
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
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
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.
The Solution
LiLaGPT, an Insurance-trained Small Language Model that Makes Insurance Data Usable at Scale
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.
Effortlessly connect with your favorite tools. Whether it's your CRM, email marketing platform.
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.
Effortlessly connect with your favorite tools. Whether it's your CRM, email marketing platform.
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.
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.
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
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
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
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.
Testimonial
What Our Insurance Partners Say
Hear how insurers accelerated data workflows, improved accuracy, and modernized operations with InsOps.
Effortlessly connect with your favorite tools. Whether it's your CRM, email marketing platform.
FAQ
Frequently
Asked Questions
Have questions? Our FAQ section has you covered with
quick answers to the most common inquiries.
Effortlessly connect with your favorite tools. Whether it's your CRM, email marketing platform.
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?
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?
Join Us Now
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
Join Us Now
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
Join Us Now
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
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