MCP server development
We build custom Model Context Protocol servers that connect AI models to your real systems: APIs, databases, and internal tools, with OAuth where it's needed and permission scoping throughout. MCP is the cleanest plumbing available for giving a model access to your business, and most of the work is deciding what it should not be able to reach.
No sales script. You talk to the engineers who'd build it.
Our team works a shifted day so you get real-time standups and same-day turnarounds in your time zone, not next-morning replies.
Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.
You work directly with the engineers building your system. No account managers sitting between you and the people writing code.
We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.
What we build
Concrete systems we ship, tuned to your data and your stack.
Custom MCP servers
Your APIs, databases, and internal tools exposed to models through a clean, standard protocol.
OAuth-secured access
Proper auth flows and token handling, so per-user permissions survive the trip through the model.
Scoped tool surfaces
Narrow, well-described tools beat a giant surface. Fewer tools chosen well means better model behavior.
Production hardening
Rate limits, audit logging, and injection-aware design for servers exposed to model-generated calls.
How we work
Scope & evals
We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.
Build in the open
Weekly demos against real data. You see progress every week and can change direction before it gets expensive.
Ship & instrument
We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.
Hand off or stay
Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.
Questions, answered
What is MCP and do we actually need it?
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The Model Context Protocol is a standard way to connect models to tools and data. If your model needs to reach your systems, MCP is usually the right plumbing, and the standardization means the same server works across clients rather than being welded to one vendor.
How do you handle permissions and security?
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Per-user OAuth where the data is user-scoped, least-privilege service credentials otherwise, and audit logging on every call. We also design assuming tool inputs are untrusted, because model-generated arguments are exactly that.
Can you connect models to our internal systems?
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Yes, that's most of this work. Internal APIs, databases, ticketing, CRM, and file stores. The technical part is usually straightforward; the valuable part is scoping what the model should be able to do.
Will this lock us into one AI provider?
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No, which is much of the point. MCP is a shared protocol, so a server you own works with different model clients. It's one of the few AI integration decisions that actively reduces lock-in.
Let's scope your build.
Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.
Start the conversation