Distance
Built too far from the work
Systems designed from briefs and boardrooms by people who never sat where the work happens. They automate an imagined workflow, not the real one.
Forward-deployed AI engineering
We embed.We ship.You own it.
We put senior engineers inside your operation, ship custom internal software, automations, and AI systems in weeks, and hand you the keys: code, prompts, infrastructure, evals. Everything. Yours.
51.5074° N / 0.1278° W
02Why AI stalls
Most AI never reaches the work.
Inertia
Piloted forever
The demo impresses. The pilot extends. Production stays next quarter. Nothing reaches the people who would use it.
Dependence
Rented, not owned
The workflow ends up behind someone else's subscription, someone else's roadmap, someone else's switching costs.
AI doesn't fail in demos. It fails in deployment. We work where it fails.
03The model
How forward-deployed works.
EMBED.
Forward-deployed means present. We sit inside the workflow with the people who run it. Discovery happens on the ground, not in a brief, and the system that gets built is the one the work actually needs.
SHIP.
Implementation means production. The only deliverable is a running system with real users on real data. Not a roadmap. Not a pilot. Not a deck.
OWN.
Handover is the finish line. Code, prompts, infrastructure, evals: in your cloud, your repos, documented so you never need us again. Choosing to keep us around is the only lock-in we believe in.
Weeks, not quarters · fixed scope · small senior team
London · UTC · Remote
04Engagements
Four ways in.
Discovery Sprint
Duration · 7–10 days
We embed, map the workflow with the people who run it, and de-risk the approach with a working prototype.
Production Build
Duration · 2–4 weeks
The first real system, shipped to real users. End-to-end workflow, integrations, deployed.
Production Hardening
Duration · 3–6+ weeks
SSO, RBAC, audit logs, evals, reliability. For when the system must survive enterprise reality.
Embedded
Duration · Ongoing
Forward-deployed engineering inside your team, sprint after sprint. Custom scope, by conversation.
Full scope and deliverables on the approach page.
For enterprise
Buying for an enterprise team? The pilots stalled on delivery, not the model. We ship one workflow to production and hand it over, so ownership is the security posture.
05Proof
We deploy on ourselves first.
The fastest way to judge an AI engineering studio is to use something it runs in production. Both are live: one public, one in private beta.
№ 17057272
06Thinking
Field notes on production AI.
Analysis
Why AI pilots fail
MIT found 95 percent of enterprise GenAI initiatives return nothing. The real failure modes behind the number, and what the successful 5 percent do differently.
Playbooks
The ownership handover: what you should hold when the vendor leaves
Code alone is not ownership. The six-part handover standard for AI systems: code, prompts, infrastructure, evals, credentials, runbooks, and how to demand it.
Definitions
What is forward-deployed AI engineering?
The working definition of the discipline, where the term comes from, why the largest AI companies adopted it in 2026, and how to buy it well.
More field notes in Thinking.
07 · Start
Put an engineer
inside your workflow.
Start with our Discovery Agent: a handful of questions, a structured brief, and a Workflow Audit booked straight into the calendar. Prefer to skip the conversation? Book the audit directly.