MCP & AI Integrations
Model Context Protocol servers, AI agents, and LLM-native integrations into your business systems.
What we do for you
We build Model Context Protocol servers and AI integrations that let LLMs do real work in your business — not just chat. MCP gives Claude, GPT, and other models a structured, audited way to call your internal APIs, query your databases, and act on your systems with permission gating and full traceability.
Our team has shipped MCP servers for code search, internal documentation, ticketing systems, and database query layers — each with fine-grained tool permissions, prompt-level access controls, and audit logs that satisfy compliance. We write servers in TypeScript using the official MCP SDK, and we deploy them as stdio or SSE depending on your client topology.
Beyond MCP, we build full agent workflows — multi-step planners that retrieve, reason, call tools, and self-correct — using Anthropic's API, OpenAI's function calling, and open-source frameworks like LangGraph when the architecture demands it.
What's included
- Model Context Protocol server development
- Tool definitions with permission gating
- Multi-step AI agent workflows
- Anthropic API & OpenAI function calling
- stdio & SSE transport implementations
- Audit logging & prompt-level access control
- Eval harnesses for agent reliability
The specifics
Every engagement draws on a specific combination of these capabilities — applied by engineers who've done it in production.
MCP Server Development
TypeScript MCP servers with stdio or SSE transport and typed tool schemas.
AI Agent Workflows
Multi-step planners with retrieval, reasoning, tool calls, and self-correction.
LLM Integration
Anthropic API, OpenAI, and open models wired into existing business systems.
Tool Calling
Function and tool definitions with Zod schemas and strict input validation.
Permission Gating
Prompt-level access controls, RBAC, and per-tool authorisation policies.
Audit Logging
Full traceability — prompts, tool calls, responses — for compliance review.
MCP & AI Integrations in production
Real projects. Real outcomes. No case study padding.
Tools chosen for the job,
not the trend.
Every technology decision in a MCP & AI Integrations engagement is made against real constraints — performance targets, compliance requirements, team familiarity, and long-term maintainability. We'll tell you which choices are wrong for your situation.
Start a
MCP & AI Integrations
project
Tell us what you're building and what's blocking you. We'll be direct about whether we're the right team — and what a timeline and cost looks like.