MCP Development
Direct production access
Tools and agents connect straight to production systems with no mediation layer.
AI platform engineering
MCP Development for Enterprise AI Agents
MCP is how you give an AI controlled access to tools and data without handing over raw credentials. We build custom MCP servers that expose exactly the operations an agent needs (read this, write that, nothing more) with authentication, scoping and logging built in. It's the difference between a governed integration and a dangerous one.
Architecture
TLS 1.3 / mTLS
NIST Aligned
Zero Public Egress
AI challenges
MCP Development challenges we solve.
Most AI pilots stall at the security review. To reach production, you need to decide up front what data the AI can see, what it costs to run and how each answer is logged.
Custom, unaudited connectors
One-off integrations are built without consistent access control or logging.
No least-privilege model
Any connected agent can reach more tools than its task actually requires.
Solution overview
MCP Development designed for production readiness.
We build custom MCP servers that expose exactly the operations an agent needs (read this, write that, nothing more) with authentication, scoping and logging built in. It's the difference between a governed integration and a dangerous one.
MCP server architecture
Define exactly which tools, APIs and data the server exposes, and their schemas.
Custom connectors
Build connectors to your existing systems instead of requiring a rebuild.
Least-privilege access
Scope every tool call so agents reach only what their task requires.
Audit logging
Log tool usage so access patterns can be reviewed and scopes adjusted over time.
How we build it
AI that runs inside clear security limits.
We set the network limits, the tools each agent may use, sign-in for every call and full logs. All of it is in place before launch.
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Understand the business goal, current environment, risks and constraints before any design work begins.
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Design the target operating model, controls and integration points so the solution fits how the business actually runs.
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Build and configure the solution using repeatable, reviewed engineering practices, not one-off manual work.
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Test against real scenarios and security requirements before anything reaches production.
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Hand over documentation, runbooks and support so the client team can run the result with confidence.
Delivery model
5 Phases
A clear check at every step
Each step ends with a written result and a clear yes or no before the next one starts.
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Nothing goes live without your sign-off
Each change must pass automatic checks and your review before it reaches production.
No lock-in
Built in your own Git and cloud accounts
Built as code
Terraform or Bicep you can rerun
Runbooks
Your team learns to run it
Our method
From first check to safe rollout.
At each step we test accuracy, speed, safety and where the data is stored. Only then do we open the tool to more people.
Define tool contracts
Specify exactly which tools, APIs and data the MCP server will expose, and their schemas.
Design access control
Build authentication and least-privilege scoping into the server, not as an afterthought.
Build the mediation layer
Implement the MCP server and connect it to the target systems and AI clients.
Audit and refine
Log tool usage and adjust scopes as real usage patterns emerge.
What you gain
Faster work, with your data kept safe.
Your teams get answers they can trust. Private data stays private, and you stay within the rules you must follow.
Mediated tool access
MCP servers sit between agents and production systems as a controlled access layer.
Consistent connector pattern
Custom connectors follow one architecture instead of ad hoc integrations.
Least-privilege by design
Scoped access and audit logging limit what each connected agent can reach.
AI tools
The AI tools we work with.
Well-known AI platforms we set up for fast, safe answers, with data kept in the region you choose.
MCP
Entra ID
APIM
Azure AI Foundry
AI agents needed real tools. The security team needed to sleep at night.
AI agents got only the tools they need, through one gateway, with every call logged.
Read the story
AI FAQ
Common questions before you deploy AI.
Short answers on testing, security limits and when AI is ready for real users.
What's the advantage over direct API access?
MCP puts a governed, auditable layer between the AI and your systems, so access is scoped and logged rather than open.
Can you connect to our internal APIs?
Yes, that's the main use case: mediated, least-privilege access to your existing services.
Further reading
Official sources behind this page.
- Model Context Protocol Model Context Protocol
- Azure API Management key concepts Microsoft Learn
- OWASP Top 10 for LLM applications OWASP
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