We wire assistants to your systems through MCP so they act on real, governed context rather than guesses.
MCP is how we let an assistant do real work instead of guessing. Rather than pasting data into a prompt, we expose governed tools — a CRM lookup, an inventory query, a document store — that the model can call, with permissions and audit logging around each one.
The problem MCP solves
An assistant that can only read what you paste into it is a toy. An assistant wired directly into production systems with broad credentials is a liability. Model Context Protocol is the middle path: an open standard for exposing specific, permissioned tools that a model may call — a customer lookup, a stock query, an availability check — with everything else out of reach.
That distinction is the difference between an AI feature you can put in front of customers and one your security team will rightly block. It also removes the most common source of wrong answers, which is a model guessing at facts it was never given.
How we implement it
Each tool is defined narrowly: what it can read, what it can write, what it must never touch. Permissions are scoped per tool rather than per system, so an assistant able to check an order status cannot issue a refund. Every call is logged with its inputs and outputs, which gives you an audit trail that reads as a sequence of actions rather than an opaque conversation.
Because MCP is an open standard, these integrations are portable. If you change model provider, the connection layer survives — which matters in a market where the leading model changes more often than your business processes do.
Where it fits alongside conventional integration
MCP is not a replacement for proper systems integration; it depends on it. If your inventory data is unreliable, exposing it to an agent produces confidently wrong answers faster. We usually sequence the work accordingly: fix the integration and the data model first, then add the intelligence layer on top of something trustworthy.