Your product,
ready for AI agents
We build MCP servers that let AI assistants and agents use your product and data, with sign-in, scoped permissions and a record of every action, so you decide what they can reach and do.
Thanks, we’ll be in touch soon.
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You’re in good company
A few of the companies we’ve worked with.
Assistants are asking to use your product
When someone asks an AI assistant for something that lives in your product, the assistant needs a way in. Without one, people copy and paste between windows, or the assistant guesses. Giving it a proper connection is quick to start. Deciding what it may see and do, for whom, and how you’ll know what it did, is the real work.
One standard way in
The Model Context Protocol (MCP) is an open standard for connecting AI applications to the systems they work with. Your MCP server describes what an assistant can do (tools), what it can read (resources) and ready-made instructions people can pick (prompts). Build it once, and any AI application that supports MCP can connect to it, though not every one supports every part.
How MCP works, in more depth: MCP explained →
What we build
The connection, and everything that makes it safe to use.
A server for your product
Your product’s key actions and data, offered to your customers’ AI assistants through MCP.
Servers for your own systems
Your team’s assistants connected to the tools you run on, such as your CRM, documents and tickets.
Around either one
Sign-in and permissions
Each person signs in and acts as themselves, with scopes for what they can read and what they can change.
Tools designed for agents
Clear names, descriptions and inputs, so an assistant can pick the right action and fill it in.
Confirmation before changes
Actions that change or delete data ask a person first, with what will happen in plain words.
Testing with real clients
Each tool tried in real AI assistants, including requests meant to misuse it.
A record of every call
Who asked, which tool ran, with what, and what came back, kept where your team can review it.
Reviews of servers you didn’t build
Third-party MCP servers checked before your team installs them: what they can reach, and who runs them.
- Agents that complete the work:AI Agents →
- The APIs underneath:Systems & API Integration →
- Testing AI against misuse:AI Security & Governance →
Read first, change with care
We start with what an assistant can read, then add actions one at a time. Each action gets the narrowest permission it needs, and anything that changes data shows a person what will happen before it runs. The server checks every request itself, so it never relies on an assistant to stay within its limits.
An assistant asks
| Tool | Access | When it runs |
|---|---|---|
| Find orders | Read | At once |
| Read an order | Read | At once |
| Cancel an order | Change | After a person confirms |
Your open orders
Shown at once, as a read.
Cancel this order?
This cancels the order. It can’t be undone.
Canceled, approved by you
Nothing changed
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What can go wrong, and how we design for it
Text an assistant reads can carry instructions it shouldn’t follow, in a message or a document. An assistant can’t always tell them apart, so nothing it reads can grant a permission: permissions are checked on the server, and the actions that matter wait for a person.
How we test AI against misuse: AI Security & Governance →
Start with one job
We pick one thing an assistant should be able to do in your product, and design the tools for it. It goes live read-only first, with a small group of users, then gains actions as you see how it’s used. You get a working server, the permissions behind it and a record of what it did, before deciding how far to take it.
Questions worth asking
Do we need an API first?
Usually yes. An MCP server most often sits on top of an API, so the same rules apply wherever a request comes from. If your API isn’t ready, we can start there. Systems & API Integration →
How is MCP different from an API?
An API is built for developers, who read its documentation and write code against it. An MCP server lists its tools, each with a name, a description and the inputs it takes, so an AI assistant can find the right one and call it, with a person able to say no.
Which AI assistants can use it?
Any AI application that supports MCP can connect to it, though not every one supports every part. Before launch, we test your server in the assistants your users have.
Can we limit what an assistant can do?
Yes. Each tool has its own permission, each person acts as themselves, and actions that change data can wait for a person’s confirmation.
Will our data be used to train AI models?
That depends on the assistant, not on MCP. What your server returns becomes part of the conversation in that assistant, so its provider’s terms decide how it’s kept and used. We check those terms for the assistants you plan to support, and keep what each tool returns to what the task needs.
Do you also build the agent?
We can. AI Agents →
Can you check the MCP servers our team already uses?
Yes. We look at what each one can reach and who runs it, and suggest which to keep.
Let’s connect it better
Tell us what an assistant should be able to do in your product. We’ll suggest a sensible first step.
Thanks, we’ll be in touch soon.
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