A Multi-Agent Orchestration Framework for Venture Capital Due Diligence
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917490714476544 |
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| author | Alexandrou, Grigorios Pramatari, Katerina |
| author_facet | Alexandrou, Grigorios Pramatari, Katerina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_13110 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Multi-Agent Orchestration Framework for Venture Capital Due Diligence Alexandrou, Grigorios Pramatari, Katerina Multiagent Systems Artificial Intelligence Information Retrieval 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. |
| title | A Multi-Agent Orchestration Framework for Venture Capital Due Diligence |
| topic | Multiagent Systems Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2605.13110 |