Most AI projects stall in the same place. The demo works. Then nobody can say what it costs to run, who owns it on a Tuesday night, or how it behaves when the model returns nonsense. We treat model work as product work. Same discovery, same reviews, same release process as everything else we ship.
Below is the whole AI map. Every line is work we do in house.
What we build with models
- LLM development services — retrieval, prompting and evaluation, the parts that decide whether an answer is any good
- Agentic AI development — software that takes actions inside your tools, inside limits you set
- AI chatbots and assistants — support and sales assistants that hand off to a person instead of guessing
Where we have shipped it
Sector context is most of the work. The same retrieval pipeline is a compliance problem in one industry and a latency problem in another.
How AI changes our own delivery
- AI engineering kit — the tooling, review rules and guardrails our engineers work inside every day
- AI readiness assessment — a short review that names what to build first and what not to build at all
- Productivity gains, measured — what the tooling saves, counted on real delivery rather than on a vendor slide
What we will not do
We will not put a model in front of a decision you cannot afford to get wrong without a human in the loop. We will not ship a feature we cannot evaluate. And we will not call something AI when a database query would answer it faster, cheaper and correctly every time.
Where this work starts
Usually with an existing product. AI features land on top of a codebase, a data model and a support team that already exist, which is why this sits beside web development and SaaS and product development rather than in a separate lab.
Not sure which line above you need? Start with the readiness assessment — it is two weeks and it ends with a written plan, not a pitch. The rest of what we do is on the services map.