AI Integration Services
AI integration services connect AI models to the software a business already runs — databases, Microsoft 365, accounting, CRMs and line-of-business systems — so AI works with your data rather than beside it. This is the part of AI most tools skip and most projects underestimate, and it is the part York Apps has been doing, in one form or another, for 25 years.
The systems are the moat. Anyone can call a language model; the value is in wiring it safely into the messy, real estate a business actually runs on. Two decades of integrating bespoke databases and line-of-business software is precisely the discipline that AI integration demands — which is why our existing custom database and software development work leads directly here.
What is AI integration?
AI integration is the engineering that lets an AI model read from and write to the systems you already use, safely and with the right permissions. Without it, AI is a clever assistant in a separate window; with it, AI becomes part of how work actually flows through your business.
Integration is where most of the real work — and most of the risk — sits. Getting a model to answer well is comparatively easy; getting it to read the right record, respect who is allowed to see what, and write back without corrupting anything is the hard, valuable part.
Integrating AI with existing and legacy systems
Most UK businesses do not run on a clean, modern API. They run on a mix — an Access database from years back, spreadsheets that became systems of record, a bespoke package built for how the business actually works. AI can absolutely work with these, but it takes integration experience rather than a plug-in.
Our approach is to meet the estate where it is: read from legacy stores without ripping them out, add a safe layer for the AI to work through, and modernise only where it earns its keep. See our field notes on patterns that actually work for legacy integration.
How AI reads your business knowledge: RAG and the Model Context Protocol in plain terms
There are two main ways to get your business’s own knowledge into an AI system. RAG (retrieval-augmented generation) lets the model look things up in your documents at the moment it answers, so replies are grounded in your data and stay current as that data changes. The Model Context Protocol (MCP) is an emerging open standard for connecting AI to tools and data sources in a consistent, governed way — think of it as a common adaptor between the model and your systems.
For most businesses, RAG plus good integration beats fine-tuning: it is cheaper, easier to keep current, and does not bake stale facts into a model.
RAG vs fine-tuning
| RAG (retrieval) | Fine-tuning | |
|---|---|---|
| What it does | Looks knowledge up at answer time | Bakes knowledge into the model |
| Keeping it current | Update the documents | Retrain the model |
| Cost to start | Lower | Higher |
| Best for | Facts, documents, changing data | Fixed style, format or behaviour |
More detail in our guides on RAG vs fine-tuning and why MCP matters for business AI.
Keeping data safe: permissions, UK GDPR, self-hosted options
Connecting AI to your data raises the stakes on permissions and compliance, and we treat that as infrastructure rather than an afterthought. That means the AI only ever sees what the person using it is allowed to see, every access is logged, and processing has a lawful basis under UK GDPR and the Data Protection Act 2018.
Where data is sensitive or must not leave your control, we can build on self-hosted models and infrastructure so nothing is sent to a third-party cloud. This is often the deciding factor for firms in regulated sectors, and it is an option many off-the-shelf tools simply do not offer.
Typical integration projects and costs
Integration projects range from connecting AI to a single system (a CRM, a document store) to a coordinated integration across several. Cost is driven mostly by the number of systems, the state of the data, and compliance requirements. As with all our work, we start with a fixed-fee consultancy that produces a costed roadmap before any build begins.
Frequently asked questions
Can AI work with our old or bespoke database?
Usually, yes — including Access databases, spreadsheet-based systems and bespoke packages. It takes integration engineering rather than an off-the-shelf connector, which is exactly the work we specialise in.
Is our data sent to a third party like OpenAI or Anthropic?
Only if you choose an approach that uses their cloud models, and even then within contractual data-processing terms. Where data must stay in-house, we build on self-hosted models so nothing leaves your environment.
What is the difference between RAG and fine-tuning?
RAG looks your knowledge up at the moment of answering, so it stays current as your data changes; fine-tuning bakes knowledge into the model and needs retraining to update. For most business knowledge, RAG is cheaper and easier to maintain.
Do we need to replace our existing systems to use AI?
No. The point of integration is that AI works with what you already have. We modernise only where it clearly pays back, not as a precondition.
Written by Austen Jones, Managing Director at York Apps. Published 6 May 2026 · Last updated 8 July 2026.