Every AI, wired into your systems. Safely, through one protocol.
We design and build production MCP servers: the standard interface that lets Claude, ChatGPT, Copilot, and your own agents query your data and act in your systems. Scoped, audited, reversible.
One server. Every assistant.
MCP collapses the matrix. Build one server per system and every MCP client can use it. That is the entire pitch.
This is all it takes. Watch an agent call your systems.
A real MCP session: the assistant connects, discovers your tools, and pulls a live answer out of your finance system, with auth on every call. No copy-pasting into a chat window. No stale export from last quarter.
Which side of the protocol are you on?
Expose your product
Your customers now bring an AI assistant to work. We build the MCP server that makes your product a first-class tool inside Claude, ChatGPT, and Copilot, so "ask your AI" becomes a feature of your platform, not a threat to it.
- →Tool surface designed from your API, not a 1:1 endpoint dump
- →OAuth 2.1 authorization wired into your existing identity
- →Registry listing and distribution to every MCP client
Wire up your stack
Your internal agents and copilots get governed access to the CRM, databases, ticketing, and ERP through one audited gateway instead of thirty brittle connectors nobody owns.
- →Connectors to the systems you already run
- →A central MCP gateway with policy, quotas, and kill-switches
- →An audit trail for every single tool call
The whole layer, not just a server.
Custom MCP servers
Production servers over your systems: resources, tools, and prompts modelled on how agents actually work, not on your org chart.
MCP gateway & governance
One managed entry point for every server in the fleet: authentication, policy, rate limits, and observability in a single plane.
Authentication & authorization
OAuth 2.1 flows, short-lived scoped tokens, per-user and per-tool permissions mapped to the access model you already trust.
Tool design & evals
The tool surface is a product. We name, describe, and schema every tool, then run eval suites proving agents pick the right one.
Deployment & operations
Remote servers on streamable HTTP, autoscaled in your VPC or ours, with tracing, metrics, and versioned rollouts.
Migration & retrofit
Wrap the REST and GraphQL APIs you already have, then retire the bespoke per-assistant integrations they were feeding.
An AI holding your keys deserves paranoia by design.
Most MCP pitches stop at “secure & scalable.” Here is what that actually has to mean before we let an agent anywhere near a production system.
OAuth 2.1 with short-lived, per-user, per-tool tokens. An agent holds exactly the access its task needs. Never a master key.
Allow-listed tool surface with hard read/write separation. What is not exposed does not exist as far as the model is concerned.
Destructive and high-value actions pause for human sign-off. The agent drafts; a person with authority commits.
Every call, argument, and result logged and replayable. When compliance asks who did what, the answer is a query away.
Tool outputs fenced, descriptions sanitized, and the whole surface tested against prompt-injection and tool-poisoning suites before launch.
Per-tenant isolation, rate limits, and egress control. One misbehaving session cannot become everyone’s problem.
Five steps. Read-only until it earns write.
Map
Inventory your systems and every AI surface that should reach them. Pick the pilot with the shortest path to value.
Design
Tool surface, schemas, scopes, and the auth model, reviewed against how your agents will actually call it.
Build
The server, its connectors, and an eval harness that proves tools get chosen and called correctly.
Harden
Injection suites, load tests, audit trails, approval gates. Security review before any write scope goes live.
Run
Observability, versioned rollouts, and new tools as your stack grows. We operate it, or hand it over clean.
Priced in the open. Fixed before we write a line.
Pilot
One system, read-only tools, live inside Claude, ChatGPT, or Copilot.
- →Single MCP server, up to 5 tools
- →SSO-backed auth
- →Eval report you can show your board
Production
Multi-system server or small fleet with write actions, approvals, and full hardening.
- →OAuth 2.1 with scoped tokens
- →Approval gates & audit trail
- →Injection-tested before launch
- →Runs in your VPC
Platform
An MCP gateway and governed server fleet as the standard AI access layer for the company.
- →Central gateway & policy plane
- →Fleet observability
- →New servers as your stack grows
The things engineering leaders ask first.
What is an MCP server?
An MCP server is a service that exposes your systems (databases, CRMs, APIs, file stores) to AI applications through the Model Context Protocol, an open standard. Any MCP-capable assistant (Claude, ChatGPT, Microsoft Copilot, Cursor, or your own agents) can then discover its tools and call them with proper authentication, instead of needing a bespoke integration per assistant.
How is MCP different from function calling or a plain API?
Function calling wires one model to one set of functions inside one application. You rebuild it for every assistant. MCP standardizes the whole interface: discovery, invocation, auth, and streaming. You build one server per system, and every MCP client can use it. The integration count drops from clients-times-systems to one per system.
Is it safe to give an AI access to production systems?
Only if the access layer is engineered for it. Our servers use short-lived scoped OAuth 2.1 tokens, allow-listed least-privilege tools, human approval gates on destructive actions, and a full audit trail of every call. Pilots start read-only; write scopes ship only after the surface passes prompt-injection and tool-poisoning test suites.
Which AI assistants support MCP?
Claude (web, desktop, and Claude Code), ChatGPT, Microsoft Copilot, Cursor, Gemini-based tools, and most agent frameworks. The protocol is an open standard governed by the Linux Foundation, with official SDKs in ten languages including TypeScript, Python, Java, Kotlin, C#, Go, Ruby, Rust, and Swift. A server you build once works across all of them.
How long does an MCP server take to build?
A read-only pilot over one system is live in two to three weeks. A production deployment with multiple systems, OAuth 2.1, approval gates, evals, and hardening typically runs six to ten weeks depending on how many systems and how strict the compliance envelope.
Remote or local MCP servers: which do we need?
Enterprises almost always want remote servers: hosted in your VPC on streamable HTTP, behind your SSO, reachable by every licensed assistant in the company. Local (stdio) servers make sense for developer tooling on individual machines. We build both; most engagements are remote.
Can you wrap the APIs we already have?
Yes, that is the typical path. Your existing REST or GraphQL APIs become the backend; we design the MCP tool surface on top. The important work is that design: a good tool surface is modelled on agent tasks, not a one-to-one dump of your endpoints.
What does MCP development cost?
Pilots start at $7,500 and are live in two to three weeks. Production deployments with auth, approvals, and hardening start at $25,000. A company-wide gateway and server fleet is scoped custom. Fixed price, agreed before we write a line of code.
Put your systems on the table.
Tell us which systems your AI should reach and what it must never be able to do. We’ll scope the pilot on the first call.