AI engineering studio
AI systems that
survive contact
with production
Agents that take real actions. Automations that run unattended. Applications built around the problem you actually have. For teams who are past the prototype and need the thing to work on the thousandth run.
- Agents
- That act, not chat
- Pipelines
- That run unattended
- Products
- Live in production
What we do
Three kinds of problem
Most engagements start as one of these and turn out to involve at least two.
AI Agents
Autonomous systems that carry real work end to end.
- Tool-using agents
- Research and analysis agents
- Document and intake agents
Workflow Automation
Manual processes rebuilt as pipelines that run themselves.
- Document processing pipelines
- Data enrichment and cleanup
- Multi-system orchestration
Applications
Custom software where nothing off the shelf fits.
- Internal operator platforms
- AI-native SaaS products
- Data platforms and dashboards
Selected work
Systems running in production
VetBid
An AI business-development platform that reads federal solicitations and tells veteran-owned firms which ones they can actually win.
Next Door Notice
A geospatial monitoring platform that watches every planning application in London and tells people when one lands near them.
AwesomeGene
A fully automated video production pipeline: one brief in, a finished, published video out, with no human in the middle.
How we think
Opinions, held for reasons
Four positions that shape every engagement. If you disagree with all four, we are probably not the right studio for you — and that is worth finding out in the first call rather than the third month.
- 01
Evaluation before enthusiasm
We build the test set before the system. If we cannot measure whether it works, we have no business claiming it does — and neither does anyone selling you an AI roadmap.
- 02
Boring where it counts
The model layer moves fast; your data model should not. We keep the novel parts contained and the foundations deliberately conventional, so a model swap is never a rewrite.
- 03
Shipped beats impressive
A narrow thing running in production teaches you more in a week than a broad prototype teaches you in a quarter. We optimise for the former, every time.
- 04
You own everything
Your repository, your cloud accounts, your data, documented for whoever inherits it. There is no layer you have to keep paying us to keep the lights on.
Writing
Notes from the build
AI Agents vs Chatbots: The Difference That Actually Matters
The distinction is not conversational quality or model size. It is whether the system can take an action that changes something — and everything hard about agents follows from that.
4 August 2026 · 6 minWhy AI Agents Fail in Production (And What Actually Fixes It)
The prototype worked. Six months later nothing has shipped. The failure is almost never the model — it is five specific engineering gaps, and they are all avoidable.
21 July 2026 · 6 minHow to Evaluate an AI Agent Before You Trust It
You cannot ship what you cannot measure, and “it seemed good when I tried it” is not measurement. A practical method for testing non-deterministic systems.
Tell us what is slowing you down
A short conversation is usually enough to tell whether this is a build, an automation, or something you should not do at all. We will tell you which.