Consultancy

AI Strategy & Implementation

AI that ships, not pilots that don't

We help small and mid-sized teams figure out where AI realistically helps, then build and run it. Strategy work, LLM and agent systems, RAG, evaluation, and the engineering to keep it honest in production.

What we actually do

Most teams we talk to don't need another AI demo. They need someone to look at their workflow, point to where AI earns its keep, and ship a working version. That is what we do.

  • AI strategy and scoping - what to build, what not to build, in what order
  • LLM, agent, and RAG systems built around your actual data and workflows
  • Evaluations and guardrails so you can ship to real users without crossing your fingers
  • The engineering and ops to keep AI features working and affordable

How we tend to work

Short cycles, working software early, and a strong bias toward the smallest version that proves the idea.

1. Discovery (1-2 weeks)

We sit with your team, look at the workflows, and write up where AI realistically helps and where it does not. You get a short doc with priorities and rough costs.

2. First slice (2-4 weeks)

We pick the smallest useful version of the highest-leverage idea and ship it. Usually one workflow, end to end, with real evals and an internal user.

3. Expand or stop

We look at what the first slice did. Sometimes it works and we expand it. Sometimes it does not and we stop. Either is a good outcome.

4. Handover (when it makes sense)

Where it makes sense, we hand the system off to your engineers with documentation and pairing time. Otherwise we stay on as the team behind the AI features.

Things we have built

A short, honest list of the kinds of AI work we ship. Not every project uses every piece.

Retrieval-augmented generation (RAG)

Internal Q&A over docs, support knowledge bases, and customer-facing answer systems with citations.

Agentic workflows

Multi-step agents that triage, draft, file, or escalate work - tool use included where it earns its place.

AI-assisted automation

Email triage, document extraction, and internal workflows that combine LLMs with n8n and custom code.

Evaluation and observability

The unglamorous work that makes the rest of it safe to ship - eval suites, regression checks, traces.

For engineering teams

Claude Code adoption for your team

Most engineering teams that try Claude Code use 10% of what it can do. We help your team get the rest - custom agents, hooks, slash commands, MCP integrations, and the workflow changes that make it stick.

Workflow audit

We sit with your engineers, map where Claude Code fits (and where it doesn't), and write up the highest-leverage changes.

Custom setup

Hooks, slash commands, sub-agents, and MCP servers wired into your repos and tooling - not generic templates.

Custom agents & SDK work

Internal agents built on the Claude Agent SDK for code review, migration, ops, or anything specific to your codebase.

Team enablement

Pairing time with your engineers so the patterns transfer instead of dying when we leave.

Stack

Models and tools we work with

We pick the model that fits the job, not the one that is loudest this month.

Anthropic Claude (Opus, Sonnet, Haiku)
OpenAI GPT family
Google Gemini
Open-weight models for self-hosted setups
n8n, LangGraph, custom Node/Python tooling
GCP, Coolify, and hybrid hosting

Want to talk about an AI project?

Book a 30-minute call. We'll walk through what you have in mind, what is realistic, and what a first slice could look like.

No slides, no pitch deck - just a working conversation