Corbin Capital

Corbin Capital's portfolio managers struggled to extract timely insights from hundreds of complex financial documents across 43+ major holdings. Manual deep dives into PDFs, spreadsheets, and reports took weeks or months, leaving senior leaders without a unified view and unable to compare assets in real time. This bottleneck slowed the firm's ability to deliver the bespoke, highly personalized portfolio strategies promised to high-end clients.

GoML built a secure generative AI copilot powered by GPT-4 Turbo using a Retrieval-Augmented Generation (RAG) framework. The system ingested all structured and unstructured data — documents, spreadsheets, scanned reports, and audio transcripts — stored in Azure Cosmos DB and indexed via Azure OpenAI and Cognitive Search. Portfolio managers can query historical data, compare holdings, and run mathematical models (Sharpe ratios, CAGR) using natural language, with role-based access controls and full audit trails for compliance.

Portfolio leaders now access insights in minutes rather than weeks or months, enabling more personalized and timely client strategies. The copilot eliminated information silos across the team and reduced human error in document searches. A senior executive noted: 'Now, our portfolio teams spend less time searching and more time advising.'

• RAG-based copilots dramatically compress research cycles for alternative asset managers dealing with large volumes of unstructured documents.
• Enterprise-grade compliance features (role-based access, audit trails, data governance) are non-negotiable when deploying AI in financial services.
• Starting with high-impact use cases like portfolio comparison and reporting delivers immediate, measurable ROI before expanding scope.