Project Overview
RegionalFinance Corp needed an AI assistant to help their analysts process financial documents, but had strict data privacy requirements that prevented them from using cloud-based LLM solutions.
The challenge was to deploy a powerful LLM on their existing infrastructure, optimize it for their specific financial domain, and ensure it could process documents with reasonable speed on their hardware.
Challenge & Approach
The client's security requirements mandated that all data remain within their network perimeter at all times. Additionally, their existing hardware infrastructure was not equipped with high-end GPUs typically used for LLM inference.
My approach was to:
1. Select and customize an open-source LLM that could be quantized to run efficiently on CPU and consumer-grade GPUs
2. Fine-tune the model on financial services terminology and document formats
3. Design a RAG (Retrieval-Augmented Generation) system to provide context from their proprietary documents
4. Optimize the deployment architecture for their specific hardware constraints
Technical Implementation
I started by evaluating several open-source models, eventually selecting Mistral 7B as the base model. I then applied quantization techniques to reduce the model size and computational requirements while maintaining inference quality.
The model was fine-tuned on a carefully curated dataset of financial documents, regulations, and terminology, significantly improving its performance on domain-specific tasks.
To integrate with their existing systems, I developed a streamlined API service that connects to their document management system, extracts relevant information, and provides context to the LLM through a vector database implementation.
The final solution included a comprehensive monitoring system to track model performance, usage patterns, and potential drift over time.
Results & Impact
The implemented solution achieved impressive results:
• 84% reduction in token processing latency compared to their initial prototype
• Successful deployment on standard corporate hardware without requiring specialized GPU infrastructure
• 93% accuracy on financial terminology and document analysis tasks
• Complete data privacy with zero data leaving their secure environment
The system now assists financial analysts in document review, risk assessment, and compliance verification, reducing the time spent on these tasks by approximately 40%.
Lessons & Insights
This project highlighted the importance of balancing model performance with hardware constraints in enterprise environments. The careful model selection and optimization process was crucial to achieving acceptable performance without requiring expensive hardware upgrades.
The project also demonstrated that with proper fine-tuning and domain adaptation, smaller models can perform exceptionally well on specialized tasks, often outperforming much larger general-purpose models.
Finally, building comprehensive monitoring and evaluation systems proved essential for maintaining the model's performance over time and gaining the client's trust in the solution.
