AI Integration

  • Integrating AI With Legacy Software Systems: Patterns That Actually Work

    By Austen Jones, York Apps · Published 2 July 2026 · Last updated 8 July 2026 When integrating AI with legacy systems, you’ll get the furthest by treating your existing software as a constraint to work around, not a problem to immediately replace. Wrap legacy databases behind lightweight APIs, route AI queries through read-only connections,…

  • Model Context Protocol Explained: Why MCP Matters for Business AI

    By Austen Jones, York Apps · Published 2 July 2026 · Last updated 8 July 2026 The Model Context Protocol (MCP) is an open standard developed by Anthropic that gives your AI models a universal communication layer for connecting to external tools and data sources. Instead of building and maintaining custom integrations for every platform,…

  • RAG vs Fine-Tuning: Getting Business Knowledge Into an AI System

    By Austen Jones, York Apps · Published 2 July 2026 · Last updated 8 July 2026 When injecting business knowledge into an LLM, you’re choosing between two distinct paradigms: RAG dynamically retrieves context at inference time via vector embeddings and nearest-neighbor search, keeping model weights frozen; fine-tuning permanently encodes domain knowledge through gradient descent, modifying…

  • AI Chatbots for Your Website: What Works, What Fails, and What It Costs

    By Austen Jones, York Apps · Published 2 July 2026 · Last updated 8 July 2026 Your website chatbot needs to qualify leads, reduce friction, and guide visitors toward conversion — or it’s working against you. FAQ bots cost £30–£150/month but lack flexibility. RAG assistants and agentic bots scale further but require higher investment. Most…