Secure connectors that let AI assistants and agents use your business systems and data.
Stack we work in
Figures as of Oct 2026
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Model Context Protocol (MCP) is the open standard AI assistants and agents use to connect to outside tools and data.
With an MCP server, tools such as Claude, ChatGPT, Microsoft Copilot Studio, GitHub Copilot and your own agents can look up records, run searches and take approved actions in your systems, without a custom integration for each AI product. We design and build MCP servers for your APIs, databases and SaaS tools, add proper sign-in and permissions with OAuth, and run them securely so you control exactly what each AI client can see and do.
Assistants give generic answers because they have no access to your systems.
Each new assistant or agent framework needs its own custom connector.
Staff paste data into AI tools or share API keys because there is no approved connection.
Nobody can say which AI client accessed which records, or on whose behalf.
Users of your product want to work with it from Claude, ChatGPT or Copilot.
Outcomes
Tools, resources, permissions and authentication flow, agreed before the build.
TypeScript or Python server with tests, in your repository.
OAuth 2.1 with your identity provider and per-user permissions.
Deployment, logging, tracing and alerts, defined in code.
Threat model and test results covering prompt injection and tool misuse.
Setup instructions for Claude, ChatGPT, Copilot and your own agents.
Look up accounts, deals and activity, and log notes from any AI assistant.
Learn moreSearch wikis, document stores and policies with the user's own permissions.
Ask questions of approved datasets through safe, read-only query tools.
Learn moreRead, summarize, update and route tickets from an assistant or agent.
Give coding assistants controlled access to logs, deployments and internal APIs.
A public MCP server so your customers can use your product from their AI tools.
Expose the right capabilities, nothing more.
Connect any system your business runs on.
Built with
Treat every AI client as untrusted until proven otherwise.
Built with
Run servers reliably at scale.
Built with
Make sure it works where your users are.
Built with
Clear names, descriptions and schemas, read-only and write tools kept separate, and confirmation on risky actions, so models pick the right tool first time.
OAuth 2.1 with your identity provider, per-user permissions, input validation and defences against prompt injection and tool poisoning, with an audit log of every call.
Every server is tested with Claude, ChatGPT, Copilot Studio, VS Code and Cursor using MCP Inspector and automated tests.
Tool changes are versioned and the server is kept current with the MCP specification, so existing clients do not break when something moves.
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Eaudvisor – AI Perfume Guide and Fragrance Advisor AppWe list the tasks people want AI to do and the systems and data those tasks need.
You receive Tool list and access model
We define tools, resources and permissions, and choose hosting and authentication.
You receive MCP server specification
We build the server, connect your systems and write tests for every tool.
You receive Working MCP server
We add OAuth, permissions, rate limits and logging, and run a security review including prompt-injection tests.
You receive Security review and fixes
We test with the AI clients your teams use and tune tool descriptions for reliable use.
You receive Compatibility report
The server goes live with monitoring, and we keep it current as the MCP specification changes.
You receive Production server and runbook
Tech stack
AI and machine learning
Databases
Backend and APIs
Languages
Cloud and DevOps
Other tools
Next step
MCP servers are the connectors. AI agents are what use them to complete multi-step work.
It depends on how many systems and tools the server exposes, the authentication model, whether it runs locally or as a hosted multi-user service, and the security review needed. As a guide, an MCP server for one system usually costs $5,000 to $15,000, and a set of production servers with OAuth, a gateway and monitoring $20,000 to $60,000 or more (USD).
A small service that describes your tools and data in the Model Context Protocol format, so any MCP-compatible AI assistant or agent can discover and use them. Think of it as an API designed for AI clients.
Claude, ChatGPT, Microsoft Copilot Studio, GitHub Copilot in VS Code, Cursor and many agent frameworks, including the OpenAI Agents SDK, LangGraph and Microsoft Agent Framework. Support keeps growing, which is why one MCP server is worth more than several custom integrations.
It can be, with the right controls. Users sign in through your identity provider, the server only exposes the tools you choose, write actions can require confirmation, every call is logged, and we test for prompt injection and tool misuse before launch.
Your API is the starting point. The MCP server sits in front of it with tools shaped for AI use: fewer, task-focused operations with clear descriptions and summarized results, plus the authorization flow AI clients expect.
Local servers run on a user's machine and suit developer tools. Remote servers run in the cloud over Streamable HTTP with OAuth, and suit teams and customers. Most business use cases need a remote server.
Yes. Many software companies now offer an MCP server so their customers can use the product from Claude, ChatGPT or Copilot. We build it with per-customer authorization and rate limits.
We track spec and SDK releases, version your server and update it so existing clients keep working. Maintenance can be part of a support plan.
A single-system MCP server usually takes 1 to 3 weeks. A hosted set of servers with OAuth, gateway and monitoring typically takes 4 to 8 weeks.
Book a call, and we talk through the goal, the systems involved and any constraints under NDA. You then get a short written proposal with scope, timeline and estimate. We need the systems or APIs to expose, the actions users expect the AI to take, and your identity provider details. A first server with a few tools is typically ready for testing within two weeks.
You own the code and can publish it internally or publicly. After launch we track usage and errors, update the server as the MCP specification and client apps change, and offer a support agreement with agreed response times.
Book a call. We will review your systems and outline the MCP servers, tools and security you need.
