Enterprise AI Modernization

The Challenge

A large enterprise software company had a mature platform serving thousands of customers — but no AI capabilities. Their monolithic codebase made a full rewrite impossible, and their customers were demanding intelligent features yesterday.

The core problem: how do you give AI agents safe, controlled access to legacy databases and APIs without rewriting the systems they depend on?

The Approach

We designed and implemented a Model Context Protocol (MCP) server architecture — a layer of custom-built servers that let AI agents interact with legacy systems through well-defined, permission-controlled interfaces.

Rather than attempting a risky big-bang migration, we applied the strangler fig pattern: new AI-powered features were deployed alongside existing functionality, gradually absorbing complexity while the legacy system continued serving production traffic without disruption.

Key architectural decisions:

• MCP servers as the integration layer — each server wraps a specific domain (user data, billing, content) with typed schemas and access controls
• Incremental deployment — AI features shipped to production one at a time, each validated independently
• Zero-downtime migration — existing customers experienced no service interruptions throughout the modernization

The Result

AI features are now shipping to production continuously through the MCP architecture. The engagement has been running since September 2025, with the team operating as embedded engineers — not outside consultants.

The platform now has intelligent capabilities that would have been impossible without this architectural approach, and the pattern has become the template for all new feature development.