A Multi-Agent Orchestration Framework for Venture Capital Due Diligence
For venture capital analysts, this provides an automated tool to streamline due diligence, though it is domain-specific and incremental in nature.
The paper presents a multi-agent framework for automating venture capital due diligence, integrating LLMs with real-time web retrieval and a programmatic extraction pipeline for Greek business registry data. The system achieves structured investment intelligence with a structural fallback mechanism to reduce hallucination in financial data.
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.