GrantsMate

Researchers at SUNY Albany have developed GrantsMate, an AI-powered research support platform that combines funding discovery, collaborator identification, and institutional policy guidance into a single conversational system. GrantsMate uses a retrieval-augmented generation (RAG) architecture built on large language models and vector databases, with dense semantic embeddings and sparse keyword search routed through a central classification layer, plus a memory and relational reasoning engine for context-aware recommendations. The platform, listed at Technology Readiness Level 3 with a patent pending, is available for licensing and can be deployed in cloud or on-premises environments with third-party integrations.

GrantsMate is an AI-driven research support platform designed to streamline research workflows by integrating funding discovery, collaborator identification, and institutional policy guidance into a unified system. Researchers and research support staff often face inefficiencies due to fragmented systems when searching for funding opportunities, identifying collaborators, and understanding institutional policies. These challenges can slow down research progress and reduce funding success rates. The need for a comprehensive, integrated tool that simplifies and accelerates these processes led to the development of GrantsMate at SUNY Albany. GrantsMate is a modular, AI-powered platform that combines machine learning, information retrieval, and natural language processing to support various aspects of the research process. At its core, it leverages a retrieval-augmented generation RAG architecture supported by large language models and vector databases, enabling it to deliver personalized and explainable recommendations for users. The platform supports both dense semantic embeddings and traditional sparse keyword searches, efficiently handling diverse user queries through a central routing layer that classifies and directs requests to the appropriate modules. GrantsMate also includes a memory and relational reasoning engine, which enhances context awareness and enables personalized interactions by recalling previous queries and relevant information. This design allows GrantsMate to deliver a conversational interface that unifies multiple research support functions into a seamless experience. The system can be deployed either in cloud environments or on-premises, making it adaptable to the unique infrastructure needs of different institutions. Its modular architecture supports integration with third-party components for data processing and machine learning, facilitating customization and evolution over time. By addressing the fragmentation of existing tools, GrantsMate aims to increase efficiency in research administration, improve the chances of obtaining funding, and foster better collaboration among researchers. Photo for reference only, not a depiction of the invention. • Integrated Workflow: Combines funding discovery, collaborator identification, and policy guidance in a single platform to reduce the complexity of research support. • Advanced AI Capabilities: Utilizes cutting-edge machine learning and natural language processing to provide personalized, explainable recommendations. • Modular and Adaptable: Deployable on cloud or local systems with support for third-party integrations, enabling flexibility and scalability. • Context-Aware Interaction: Memory and relational reasoning engine allows the system to maintain context and personalize user experiences. • Unified Conversational Interface: Simplifies user engagement with complex research workflows through an intuitive conversational platform. • Institutional research funding portals to help researchers find appropriate grant opportunities efficiently. • Collaborator matchmaking tools to identify and connect researchers with complementary expertise. • Virtual assistants for research administration offices to support Q&A on institutional policies and procedures. • Customizable platforms across academic, governmental, and private research institutions. • Integration into existing research support ecosystems to enhance data processing and decision-making workflows. Patent Pending TRL = 3 This technology is available for licensing.