Toolkit for Practical, Accurate, Secure, and Reliable RAG

Organizations have been slow to adopt Large Language Models (LLMs), despite their enormous promise. áRetrieval Augmented Generation (RAG) can address many of the LLM adoption challenges. However, delivering solutions using RAG requires the integration of complex technologies and significant AI expertise.áWe will deliver a modular, scalable toolkit to help Army users build practical RAG solutions without requiring AI expertise. Our custom Technical Language Processing (TLP) approach will precisely model Army terminology and ensure best-in-class performance. We have optimized the toolset to minimize computing resource requirements, improve reliability, and mitigate LLM biases. Our innovative algorithms will satisfy security requirements (e.g., IL-5) and eliminate vulnerabilities such as data leakage. We will leverage our proven codebase and 5+ years of LLM productization experience to ensure success.In Phase I, we will deliver a minimum viable product (MVP) of a mission and systems engineering assistant. This assistant will demonstrate the RAG toolkit by reducing the cost of mission/system modeling by up to 70%. The assistant will streamline analytics such as war gaming, analysis of alternates (AoA), enterprise-level trades, or course of action analysis (COAA). These analytics will enhance missions, new acquisitions, and modifications. This MVP will help gain stakeholder participation and support prototype development in Phase II. Our experience with Army missions/systems engineering will ensure success.We have assembled a team with deep expertise in AI, Army mission/systems engineering, and program management. We will build on our proven AI models and leverage over 5 years of LLM development experience. We will leverage validated technologies from DARPA, MDA and USAF SBIRs to accelerate implementation. We have a decade-long track record of technology transition and delivering exceptional value.