We build
your AI infrastructure.

Three disciplines, one team.

We don't pretend AI exists in a vacuum. Most of our clients are also re-platforming SAP or redesigning HR, and the AI worth building usually sits on top of that work, not beside it.

How we actually work.

No discovery theatre, no five-month roadmap deck before code is written. Four phases, optimised for getting something useful in front of users early.

1. Phase 01

   Frame

   Usually the two-week AI audit. We come into the organisation, talk to the people doing the work, look at the data, and decide together whether AI is genuinely the right answer — or whether the problem is better solved differently.

   → Opportunity map · Infrastructure plan · Funding note
2. Phase 02

   Prototype

   Two to four weeks. We build a working slice end-to-end against your real data. The point is to learn what your domain demands, not to ship a deck.

   → Working prototype · Eval set · Cost model
3. Phase 03

   Productionise

   We wire in observability, ACLs, evals-in-prod, error handling, and the deployment topology you actually need. Senior engineers, not handed off to juniors.

   → Production system · Runbooks · SLOs
4. Phase 04

   Operate

   We stay long enough for the system to earn trust: running evals, tuning prompts, watching the metrics that matter, transferring ownership cleanly.

   → Eval reports · Tuning · Knowledge transfer

Anonymised case studies.

Tooling that shifts your engineers' day.

MCP servers, skills, slash commands, subagents, built for the way your team actually ships. Custom Claude Code extensions that put your APIs, your conventions, and your workflows one keystroke away.

// Custom MCP server exposing your internal APIs to Claude Code
import { McpServer } from "@modelcontextprotocol/sdk";

const server = new McpServer({
  name: "acme-internal",
  version: "1.0.0",
});

server.tool("search_tickets", {
  description: "Search the ticketing system",
  input: { query: z.string(), team: z.string().optional() },
}, async ({ query, team }) => {
  const results = await tickets.search({ query, team });
  return { content: results.map(toClaudeCitation) };
});

server.tool("deploy_preview", {
  description: "Spin up a preview environment",
  input: { branch: z.string() },
}, async ({ branch }) => {
  const env = await ci.deploy(branch, { ttl: "8h" });
  return { content: [{ type: "text", text: env.url }] };
});

server.start();

Practical guides from the work we ship.