Is it secure to expose internal systems to AI?

It is, when done properly. We implement OAuth, per-user scopes, allow-lists, rate limits and full audit logging, so assistants can only do what the user could do themselves.

Which AI tools can use the server?

Claude, ChatGPT, Cursor, VS Code, Windsurf and any custom agent that speaks MCP. One server serves them all.

Can you build an MCP server for our SaaS product?

Yes. We design the tool surface, build and host the server, publish it and keep it up to date as your API evolves.

How long does it take?

A first server over one system is typically in use within two weeks. Enterprise-grade hosting and auth follows over the next two to four weeks.

BuildEngineering & Development

Make your systems available to AI, safely

We build Model Context Protocol (MCP) servers that let Claude, ChatGPT, Cursor and your own agents read and act on your internal systems with proper authentication, permissions and audit. Your data becomes usable by AI without becoming exposed.

Enquire about MCP Development How the capacity model works

Written reply from a senior person · no pitch deck · no obligation

5 days
to first MCP server in use
OAuth
and scoped permissions as standard
100%
calls logged and auditable
Any client
Claude, ChatGPT, IDEs, agents

What we build

What MCP Development includes

Senior-owned, AI-accelerated and integrated into your stack. Everything below sits inside your monthly capacity.

Custom MCP servers

Typed tools, resources and prompts over your APIs, databases and documents, in TypeScript or Python.

Authentication and authorisation

OAuth, API keys and per-user scopes so assistants only see what the user is allowed to see.

Enterprise integrations

Connectors for CRM, ERP, ticketing, finance, document stores and internal services.

Remote hosting

Hardened, hosted servers with rate limiting, logging and monitoring in your cloud.

Testing and evaluation

Conformance tests, schema validation and scenario evals so tools behave predictably.

Rollout and enablement

Client configuration, documentation and training for the teams who will use the tools.

Process

How we run MCP Development

Capacity starts in days. Here is what the first weeks look like.

1
Week 1
Tool inventory
Map the systems and actions worth exposing, and the permissions each needs.
2
Weeks 1 to 2
First server
A working MCP server on your highest-value system, used from Claude or your IDE.
3
Weeks 2 to 4
Harden and host
Auth, scopes, logging and remote hosting, with conformance tests in CI.
4
Ongoing
Extend
More tools and systems added inside the same monthly capacity.

Fit

When MCP Development is the right call

Your team already uses AI assistants

They copy and paste from internal systems. An MCP server lets the assistant read and act directly, with permissions.

You are building agents

Agents need reliable, typed tools. MCP gives them a standard interface across your systems.

You sell software

An official MCP server makes your product usable from AI assistants and IDEs, a growing buying criterion.

Security has concerns

Centralised, audited access beats a hundred ad-hoc integrations and shared API keys.

Stack

Tools we use for MCP Development

We use what fits your business and your existing systems, and we say so when the answer is 'not this one'.

SDKs

MCP TypeScript SDK
MCP Python SDK
FastMCP

Clients

Claude
ChatGPT
Cursor
VS Code
Custom agents

Auth

OAuth 2.1
Auth0
Clerk
Okta
Entra ID

Hosting

Railway
Cloudflare Workers
AWS
GCP

FAQ

MCP Development questions, answered.

MCP is an open standard that lets AI assistants and agents connect to external tools and data through a consistent interface. One server works across many AI clients.