Project Overview
The bank wanted to leverage the immense productivity gains of AI coding assistants like GitHub Copilot. However, their strict compliance and IP protection policies prohibited any source code from leaving their network perimeter. They needed a custom AI IDE that could operate entirely within an air-gapped, on-premise infrastructure while providing enterprise-grade developer tracking.
Self-Hosted DeepSeek Model
We deployed a highly optimized, quantized version of the DeepSeek Coder LLM onto the bank's private GPU Kubernetes cluster. This ensured that all inference happened locally, guaranteeing zero data leakage.
Custom VS Code Extension
Our team developed a white-labeled VS Code extension that acts as the frontend client. It intercepts developer keystrokes, provides inline autocomplete (ghost text), and offers a chat interface for complex code refactoring and explanation.
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Enterprise Telemetry Dashboard
We built a centralized administrative dashboard using FastAPI and React. It tracks AI acceptance rates, token consumption per team, and overall developer velocity, proving a 40% increase in code output within the first quarter.
Key Challenges
Challenge 1
Zero internet connectivity available in the secure development environment.
Challenge 2
Requirement to run resource-intensive LLMs entirely on-premise.
Challenge 3
Integration with the bank's custom internal SSO and RBAC authentication.
Challenge 4
Need for deep telemetry to measure actual developer velocity gains for ROI calculation.
Results & Outcomes
The custom AI IDE revolutionized the bank's internal engineering workflow. Over 800 developers adopted the tool within 60 days. The self-hosted architecture maintained 100% regulatory compliance and IP protection, while the telemetry dashboard proved a concrete 40% reduction in time-to-merge for complex financial algorithms.