Document Processing · Enterprise Knowledge Search · Risk Management
Ardian needed to analyze large volumes of confidential due diligence material faster, but its teams were overwhelmed by scattered internal documents, NDA constraints, and traditional machine-learning approaches that did not handle unstructured text well enough for deal work.
Reported impact
Ardian launched GAIA into full production within one year, exceeded adoption expectations with more than 500 weekly active users, processed over 18,000 documents and more than 280,000 requests, and gave teams clearer information for faster and more thorough deal analysis.
Ardian built GAIA as an internal generative AI platform to help investment teams work through due diligence material faster without relaxing confidentiality controls. Teams manually upload market reports, investment memoranda, legal files, and other internal documents into the platform, which then lets users query them in natural language, summarize them, and collaborate through persistent folders. GAIA was developed with Artefact and uses a model-agnostic stack so Ardian can select among models for different tasks; Mistral models are highlighted for French-language documents and summarization. The architecture pairs document interaction and optional web search with strict security and data governance, plus manual upload steps that preserve oversight for NDA-bound information. That combination turned unstructured-text analysis into a production workflow for investment professionals and created a foundation for broader reasoning-based risk analysis across private-equity deal reviews.
Users manually upload internal due diligence files, market reports, and investment memoranda into GAIA; the platform routes natural-language queries and summarization tasks to selected models including Mistral, combines those answers with optional web search and shared folders, and returns first-pass analysis to investment teams under governed access controls.
"Ardian partnered with Artefact and Mistral to develop GAIA, an internal Generative AI platform"
"It allows extra room for higher value-added tasks"
"The platform has processed 18,000+ documents since inception and generated more than 280,000 user requests"
"The technical architecture prioritizes security and compliance, with manual document uploads providing an additional layer of human oversight"
"We needed model flexibility-so we added Mistral models, which turned out to be very efficient, especially with French documents and summarization tasks"
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
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