GiveyourAIaccesstothetoolsyourteamuses
Model Context Protocol servers that make your internal systems available to AI assistants safely. Built and operated by the pod that ships your product.
The API layer for your AI
Typed, auditable, secure. Everything your team would expect from an internal platform, not a bolt-on demo.
MCP server development
Custom servers that expose your internal APIs, databases, and knowledge to LLM assistants through typed, auditable tools.
Tool integrations
Every tool is strongly typed, versioned, and testable. Agents get reliable contracts, not screen-scraped guesses.
Context management
Scoped context delivery, retrieval, and caching. The assistant gets what it needs, not the whole database.
Authentication and authorisation
OAuth, service accounts, per-user scopes. Your access controls extend into the assistant automatically.
Multi-client support
Claude Desktop, Cursor, in-house clients, mobile assistants. One server, many consumers, consistent behaviour.
Observability
Every tool call logged with inputs, outputs, timings, and caller identity. Debug with evidence, not guesses.
From tool surface to first client
Tool surface design
Week 1Audit your internal systems. Decide which capabilities the assistant gets, and which stay off-limits.
Schema and contracts
Week 1 to 2Define typed tool schemas with clear input and output contracts. Agents call tools against a real interface, not prose.
Server build
Week 2 to 4Implement, test, and benchmark. Every tool has fixtures, every server has a load profile.
Security review
Week 3 to 4Threat model, auth flows, rate limits, audit logs. Ship with the pieces security teams want before they ask.
Deploy and integrate
Week 4 to 5Host in your cloud, wire up Claude or your assistant of choice, validate with real traffic.
Real uses we have shipped
Internal knowledge access
Let assistants answer from your docs, wiki, tickets, and runbooks without shipping your knowledge to third parties.
CRM and operations
Expose customer data, pipeline actions, and ops tooling as typed MCP tools your team already trusts.
Developer tooling
Code search, deploy triggers, incident commands. Engineering assistants wired into your real environment.
Data analysis
Natural language queries against your warehouse and dashboards with scoped access and traceable queries.
Protocols, runtimes, and hosts
MCP questions
An open standard for giving AI assistants typed, safe access to tools and data. Think of it as the API layer for AI.
It started with Claude but the ecosystem is broadening. We also build compatible OpenAPI and function-calling surfaces for other assistants.
Scoped tools, per-user auth, redaction at the server edge, and retrieval patterns that surface only what the task needs.
Yes. Many first-gen integrations work but are hard to maintain. MCP gives you typed contracts, versioning, and auth in one place.
Same pod that built it. MCP servers are long-lived infrastructure and need engineers who understand both your business and the protocol.
Related capabilities
All capabilitiesReady to ship
faster than you can hire?
30 minutes to scope, stack, and a first-sprint plan. No pitch deck, no pressure.