We collaborated with HENT to automate document-based tasks using Large Language Models (LLMs) as decision-makers, enhanced by Retrieval-Augmented Generation (RAG) for context-aware processing. This automation streamlined the extraction and assessment of information from extensive internal documentation, greatly improving efficiency.

Advanced data pipelines ensured the LLMs had real-time access to project information, optimizing their accuracy. The integration of RAG enabled efficient document handling, reducing manual intervention and setting the stage for potential future expansions of LLM-driven automation in other areas.