GRAIL: An Agentic AI Architecture for Interactive Grant Proposal Writing
GRAIL: An Agentic AI Architecture for Interactive Grant Proposal Writing
Zhisheng Tang, Mayank Kejriwal
Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence
Demo Track. Pages 8538-8542.
https://doi.org/10.24963/ijcai.2026/998
Securing research funding remains fragmented and time-consuming: researchers must navigate separate databases across dozens of agencies while simultaneously drafting competitive proposals. We present GRAIL, a web-based platform that unifies grant discovery and proposal writing through conversational AI. Users describe their research interests in natural language to explore opportunities from a unified index of 11.8K U.S. federal and nonprofit grant opportunities; within the document editor, integrated AI assistance supports real-time proposal revision and refinement. The system runs in any modern browser without installation. Conference attendees are invited to interact with the live system at the demo booth, explore the grant discovery and writing assistance workflows, and provide feedback on the user experience.
Keywords:
AI: Agent-based and Multi-agent Systems
AI: Data Mining
AI: Natural Language Processing
AI: Search
