Challenge
We developed an AI assistant capable of ingesting large volumes of financial documents and generating investor-grade insights. The assistant answers questions, summarizes documents, extracts metrics, and surfaces risks—speeding up decision-making for PE, VC, and corporate finance professionals.
Tech Challenge
Financial data must be trustworthy and traceable. Queries must return within seconds. Relevant data buried across diverse formats. The assistant must speak the language of finance. Some tasks require deep reasoning while others need simple extraction.
We developed an AI assistant capable of ingesting large volumes of financial documents and generating investor-grade insights. The assistant answers questions, summarizes documents, extracts metrics, and surfaces risks—speeding up decision-making for PE, VC, and corporate finance professionals.
Tech Challenge
Financial data must be trustworthy and traceable. Queries must return within seconds. Relevant data buried across diverse formats. The assistant must speak the language of finance. Some tasks require deep reasoning while others need simple extraction.
Solution
Real-time RAG pipeline using Milvus for financial document embeddings. Multi-model architecture using different LLMs based on task complexity. Dynamic orchestration layer routing queries to most suitable LLM. Secure ingestion of PDFs, Excel files using OCR. Fine-tuned models for EBITDA detection, risk factor extraction, and clause recognition.
Real-time RAG pipeline using Milvus for financial document embeddings. Multi-model architecture using different LLMs based on task complexity. Dynamic orchestration layer routing queries to most suitable LLM. Secure ingestion of PDFs, Excel files using OCR. Fine-tuned models for EBITDA detection, risk factor extraction, and clause recognition.
Results
55% reduction in time-to-insight for preliminary company assessments.
Analysts now spend more time on interpretation and strategy, not data extraction.
Increased deal velocity through faster screening, modeling, and memo generation.
The assistant serves as an institutional knowledge layer, preserving past research and surfacing it on demand.
55% reduction in time-to-insight for preliminary company assessments.
Analysts now spend more time on interpretation and strategy, not data extraction.
Increased deal velocity through faster screening, modeling, and memo generation.
The assistant serves as an institutional knowledge layer, preserving past research and surfacing it on demand.