MCP
Move from MCP demos to production systems
Build an MCP integration layer that lets AI assistants take real actions in your stack with clear permissions, auditability, and predictable behavior.
What teams hire us to solve
Most MCP projects fail between the first demo and real operations. We close that gap.
Reusable integration layer
Avoid rebuilding one-off connectors for every new model or app.
Security and governance built in
Use scoped permissions, audit logs, and policy controls from day one.
Reliable tool contracts
Ship typed interfaces and failure handling that models can use consistently.
Operational handoff
Deploy with monitoring, runbooks, and ownership your team can sustain.
Works with Claude, GPT, and Gemini stacks
Designed for OAuth/SSO, RBAC, and auditability
Phased rollout: pilot first, expand safely
Optional managed support after launch
A practical path from discovery to production
Scope high-value tools first, then ship a secure MCP layer your team can operate.
Scope and architecture
~1 week
Map your systems, define high-value tool calls, and set data access boundaries.
Build the MCP layer
~2-4 weeks
Implement tool schemas, authentication, logging, and guardrails around every action.
Replace fragile one-off integrations with reusable MCP endpoints.
Launch and scale
Ongoing
Deploy to your environment, monitor usage, and expand connectors based on real workflows.
FAQ
Quick answers before you start.
Do we still need MCP if we already have APIs?
Usually yes, once more than one AI workflow needs shared tool access.
Do we still need MCP if we already have APIs?
Usually yes, once more than one AI workflow needs shared tool access.
APIs expose system capabilities, but MCP standardizes how AI assistants discover, call, and safely use those capabilities across workflows.
Can one MCP setup support different models?
Yes. We design a model-agnostic integration layer.
Can one MCP setup support different models?
Yes. We design a model-agnostic integration layer.
We build MCP interfaces so your tools can be reused across Claude, OpenAI, and Gemini-based experiences without duplicating core integration work.
What makes production MCP different from a prototype?
Security, reliability, and operations.
What makes production MCP different from a prototype?
Security, reliability, and operations.
Production MCP includes authentication, permission boundaries, observability, error handling, and runbooks so teams can trust it under real load.
Start with a discovery session. We’ll define the first integrations worth shipping and a rollout path that fits your stack.
No commitment required.