The problem it solves
Integrations you do not have to rewrite.
Without a standard, every AI tool needs custom plumbing to every system. Four assistants across six systems is twenty-four integrations to build and maintain.
MCP inverts that. You expose each system once as an MCP server — describing what it can do and what arguments each operation takes — and any MCP-capable client can use it. Six servers, not twenty-four connectors. When you change model vendor, the servers do not move.
It is an open specification with implementations across the major AI clients, which matters more than any single vendor's roadmap: the connector you build is not a bet on one company staying in business.
What a server exposes
- Tools — operations the model may call, with typed arguments and a description of what each one does
- Resources — data it may read, addressed by URI
- Prompts — reusable templates your team has agreed on
The server owns authorisation and validation. The model proposes a call; your server decides whether it is allowed and whether the arguments make sense. That boundary is the security model.
What we build
Servers for the systems you already run.
ERP and finance
Read stock, post invoices, query ledgers — scoped per role, with every write validated and logged before it reaches the system of record.
CRM and ticketing
Lookup, create and update records so an agent can complete a customer request instead of drafting an email about it.
Document stores
Permission-aware access to SharePoint, Drive or a DMS, so retrieval respects the same rights the user already has.
Bespoke and legacy
The in-house system with no modern API. We wrap it carefully rather than replace it, which is usually the cheapest route to AI-readiness.
Operational data
Telemetry, IoT and warehouse queries exposed as safe, parameterised operations rather than open database access.
Internal tooling
Deployment, provisioning and reporting actions your own team can drive conversationally, with the same controls as the UI.
Security, plainly
An MCP server is a door into your systems.
It deserves the same review as any other integration point, and we treat it that way. Most of the risk is not exotic — it is the ordinary discipline of not over-granting.
Worth knowing: anything a model reads can attempt to influence it. A document, a ticket comment or a web page can contain text crafted to look like an instruction. The defence is never to trust retrieved content as a command — which is why permissions live in the server and consequential actions keep an approval step.
- Per-role credentials, never a shared superuser token
- Read and write split into separate, separately grantable tools
- Server-side validation of every argument, independent of the model
- Rate limits and value caps on anything that costs money
- Full audit log of calls, arguments, caller and outcome
- Retrieved content treated as data, never as instructions
Next step
Want your systems usable by any AI client you choose?
Tell us which systems matter most and who needs to reach them. We will scope the servers and the permission model together.
Start the conversation →