LIGER™

LMI Instruction-tuned GEnerative Resource

Secure, private, and trustworthy generative AI (GenAI) solutions for government.

Use Cases

Use Cases
LIGER™ Video
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Use Cases

Acquisition

LIGER™ assists acquisition professionals with managing numerous RFI responses by making them easily searchable, synthesizing relevant information, and generating summaries. This application enables users to quickly identify common themes and trends across industry responses, ultimately saving time and improving the overall quality of market research reports.
Intel Analyst

LIGER™ supports intelligence analysts by providing digitally enabled versions of multiple structured analytic techniques. LIGER™ empowers analysts by exponentially increasing the speed of creating insights while maintaining analytic rigor at scale.
Policy

LMI built a tool in LIGER™ to enable policy analysts to automatically compare policy guidance quickly across the organization to find gaps and inconsistencies. This tool saves time while ensuring more complete and consistent policy across the organization.

LIGER™
LIGER™ is LMI’s toolkit for building and deploying GenAI solutions to solve government challenges. LIGER™ transforms foundational large language models (LLMs) into customized, government-focused GenAI solutions for secure access to your agency’s data that aligns seamlessly with your operational processes.

LIGER™ for Acquisition
LIGER™ supports government acquisition professionals in handling a large volume of RFI responses by simplifying search capabilities, organizing relevant insights, and creating actionable summaries.

Why choose LIGER™ for your organization?

Private LLM deployment designed with government security in mind.
Up to date access to confidential information from across your agency.
Significant reduction in “hallucinations” and the ability to cite information.
Customizable user-access controls to protect “need to know” information.
Build custom workflows on existing tools to innovate.

LMI's LIGER™ (LMI Instruction-tuned GEnerative Resource) effort outlines a research and development strategy to develop and deploy compute-accessible LLMs and integrate them with domain-relevant solutions that respect data privacy and satisfy known customer challenges.