Your customers are already asking Claude, ChatGPT and their coding tools to work with your product. Without an MCP server, the answer is copy and paste. With one, the model can read their data, run their workflows and act inside your software with the exact permissions you set.
We design and build Model Context Protocol servers for software companies and for businesses with systems an AI agent should be able to reach. One server works across every MCP client, so you build the integration once instead of once per assistant. Every build ships with authentication, scoped permissions, audit logging and an eval suite that proves the model calls the right tool with the right arguments.
What we build
Remote MCP servers for SaaS products that support many users through OAuth 2.1. Local servers for desktop tools and developer workflows. Internal servers that give your own agents controlled access to a CRM, a data warehouse, a ticketing queue or a Webflow site. We run this way ourselves: our agency operations connect Claude to Webflow, email, CRM and analytics tools through MCP, and it is how much of the work on this site gets done.
Why it matters for a software company
AI assistants are turning into a distribution channel. Products with a solid MCP server get listed in connector directories, get picked when a user asks the model for a tool, and keep customers inside the product instead of exporting to a spreadsheet. A weak server, or none, means the model works around you. The results from our CannabisRegulations.ai engagement show what happens when a product is built to be found and used by AI: 3,260 sessions from eight AI assistants in nine months.
How it works
A discovery workshop produces the tool list and the permission map. We build on the official SDKs, test against real prompts, add security before launch, deploy to your cloud, and hand over documented code your team owns. Ninety days of support are included.