We present a fully automated multi-agent framework for corporate due diligence and market analysis in venture capital. The system runs on an event-driven orchestration architecture, combining Large Language Models (LLMs) with real-time web retrieval to synthesize unstructured data into structured investment intelligence. A central technical contribution is a programmatic extraction pipeline that reverse-engineers the frontend-to-backend communication of the Greek Business Registry (Γ.E.MH.), querying dynamic endpoints to retrieve official financial filings that are then parsed using a layout-aware OCR extractor. A structural fallback mechanism explicitly flags data absence rather than generating unverified figures, directly targeting hallucination in financial contexts. All workflow artifacts are publicly available to support replication.
@article{arxiv.2605.13110,
title = {A Multi-Agent Orchestration Framework for Venture Capital Due Diligence},
author = {Grigorios Alexandrou and Katerina Pramatari},
journal= {arXiv preprint arXiv:2605.13110},
year = {2026}
}