Your team already uses AI assistants but still copies data by hand. With an MCP server, the assistant looks things up or acts in your systems with the permissions you decide.
An MCP server is a connector that makes tools and data from your systems, such as your CRM or ERP, available to assistants that support the Model Context Protocol, like Claude or ChatGPT. We build it for companies that want to use AI on their own data, with least-privilege access and a log of every action. You start with a 7-day trial: a read-only server on one system.
The Model Context Protocol is an open protocol that standardizes how AI assistants connect to external tools and data sources. One server works with different compatible clients.
The client lives in the AI application, and the server exposes tools (actions such as looking up an order), data resources and prompt templates. It runs locally or remotely over HTTP; for remote servers, the specification defines OAuth-based authorization.
Each tool does one thing. We start read-only, with restricted database users or API keys, and where possible each person signs in with their own account so the assistant acts with their permissions, not an admin's.
Compatible clients usually ask for permission before running a tool, and on top of that we design any action with side effects as a draft or behind explicit confirmation. Every call is logged: who, which tool, with what input and with what result.
Your team uses Claude, ChatGPT or another compatible assistant every day, your systems have an API or an accessible database, and you want one reusable connector instead of a workaround per tool.
A realistic first step: two or three lookup tools on one system, such as finding customers or checking an order's status, connected to your team's assistant and logging every call. We validate them with real questions before discussing write actions. Scope, dates and terms are agreed in writing up front.
The protocol is the same, but each assistant integrates it in its own way, and availability depends on the app, the version and your plan. Some support local servers, others only remote ones. Before building, we check exactly what your team's setup allows, so we don't develop something you can't connect.
Only if the server exposes a tool that allows it. That's why we start read-only and keep any later actions narrow: create a draft rather than send it, update one specific field rather than any table. Sensitive operations ask for explicit confirmation, and everything is logged so it can be audited or reversed.
An API integration moves data between systems automatically, the same way every time. An MCP server offers tools to an AI assistant, which decides when to use them based on the conversation. They're often combined: the integration keeps data current and the MCP server lets people query or use it in plain language.
A local server runs on each user's computer and suits testing or individual use. For a team, the usual choice is a remote server over HTTPS on your infrastructure or in your company's name, which you pay for directly. The code, credentials and logs stay under your control.
Tell us which assistant your team uses and which system you'd like to connect. We'll reply in writing with a proposal to start read-only.
Pick a goal and we'll suggest the services that fit best.