AI & Data Solutions
AI Systems Integration & MCP
Connecting AI tools to your systems so they can look things up and, where you allow it, make changes.
Discuss Your ProjectIn plain terms
AI tools are most useful when they can look things up in your systems and, where you allow it, do things. This service builds the safe connection: a controlled doorway that lets an AI assistant read an order, find a customer or log a job, and nothing else.
Does this sound familiar?
- Each AI experiment gets its own one-off link to your systems
- Nobody can say what an AI tool is allowed to read or change
- The application that holds the data has no usable API
A typical scenario
A facilities management company
Before
Engineers in the field phone the office to ask about a site's open jobs, access codes and equipment history. The information is in a job system, a spreadsheet and a customer database that do not talk to each other.
After
An assistant on the engineer's phone answers those questions in one place. Behind it a small service connects to the three systems, read-only, and logs every lookup. Logging a completed job is allowed, but only through an approved action the office can see.
How we deliver it
From first call to running
- 1
Map the data and actions
We list what an assistant would need to read and to do, and rank each by risk.
You get: A table of data and actions, with a risk level for each
- 2
Build the connections
We create APIs or MCP servers, starting with the safe ones.
You get: Working connectors in a test environment
- 3
Lock it down
Sign-in, roles, limits and logging.
You get: A permissions model and a test showing what each role can and cannot do
- 4
Connect an assistant
We try it with a real AI tool and a few users.
You get: A working demo and feedback
- 5
Extend carefully
Actions are added one at a time, with approval for anything that changes data.
You get: A growing, documented set of tools
What we need from you
- Technical contacts for each system
- API documentation or database access
- A view on which actions must always need approval
- Your security team's requirements
Where it works best
- Several systems that staff look across by hand
- You want AI tools to use live data and not copies
- Older systems that need a modern front door
Worth sorting out first
- A single simple connection: an ordinary API may be all you need, and we will say so
- Unclear ownership of the systems involved: name an owner for each one before connecting
- Letting AI change data: start read-only, then add approved actions one at a time
How we work with you
Fixed price per connector or per system, because each is a clear piece of work. Documentation and a handover session are included.
Discuss Your ProjectTypical technology
Questions
Do we need MCP at all?
Not always. If one application needs one connection, a normal API is simpler. MCP earns its place when several AI tools need to use the same systems.
Is it safe to let AI near our systems?
It can be, with the least access possible, read-only by default, approval for anything that changes data and a complete log.
Does this need new software on our servers?
Usually a small service, hosted in your Azure subscription or ours, that sits between the AI tool and your systems. Your own systems stay as they are.
Which AI tools can use it?
MCP is supported by a growing number of AI applications. Where a tool does not support it, we offer the same functions as an ordinary API.
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