A Multi-Agent Orchestration Framework for Venture Capital Due Diligence

2026-05-13

AI Summary

This study addresses the inefficiency and error-proneness of manual due diligence and market analysis in venture capital by proposing a fully automated, event-driven multi-agent framework. The framework integrates large language models with real-time web retrieval to transform unstructured data into structured investment intelligence. It introduces a programmable information extraction pipeline that reverse-engineers frontend-backend communications of the Greek Business Registry to obtain official financial statements. By combining layout-aware OCR with a hallucination-mitigation mechanism, the system explicitly flags missing data instead of generating unverified content. The resulting end-to-end automated due diligence workflow is fully open-sourced and reproducible, significantly reducing the risk of model hallucinations in financial applications.

Abstract

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.

Problem

• due diligence
• venture capital
• financial hallucination
• unstructured data
• investment intelligence

Innovation

• multi-agent orchestration
• large language models
• layout-aware OCR
• financial hallucination mitigation
• programmatic data extraction