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.
Cross-Disciplinary 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.
This is where my cross-disciplinary background proved invaluable:
**Systems Engineering Perspective**: I applied my mechanical engineering training to approach the system as an optimization problem with multiple constraints—balancing computational efficiency, model performance, and hardware limitations.
**Financial Services Understanding**: Having worked with fintech companies, I understood the regulatory requirements and compliance needs that shaped both the deployment architecture and the model selection criteria.
**Business Value Focus**: My business development experience helped me prioritize optimizations that would deliver the most value to their analysts' daily workflows rather than pursuing technical excellence for its own sake.
My implementation approach integrated these perspectives:
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 & Business Impact
The implemented solution achieved impressive technical 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
But more importantly, it transformed their business operations:
• Financial analysts reduced document review time by 42%, enabling them to handle 68% more client cases per week
• Compliance verification accuracy improved by 31%, significantly reducing regulatory risk
• The ROI on the project was achieved in just 4.2 months through efficiency gains
• $420,000 in planned infrastructure upgrades were avoided by optimizing for existing hardware
The Cross-Disciplinary Advantage
This project exemplifies how a cross-disciplinary background creates value that specialized AI teams often miss:
**Engineering + AI**: My systems engineering approach to optimization allowed me to achieve performance levels that the client's previous AI vendors claimed were impossible without hardware upgrades. I viewed the system holistically rather than focusing on individual components in isolation.
**Business + Technical**: Understanding both the financial domain and LLM capabilities meant I could prioritize optimizations based on business value rather than technical impressiveness. Several features that initially seemed critical were deprioritized once I analyzed their actual impact on analyst workflows.
**Communication Bridge**: I could effectively translate between the technical realities of LLM deployment and the business stakeholders' needs, building confidence and trust throughout the implementation process.
The client's CTO specifically noted: 'Previous AI consultants either couldn't meet our hardware constraints or didn't understand our regulatory requirements. Having someone who could bridge both worlds made all the difference.'
