Two ways to work together.
One standard.
Some organisations need a delivered outcome — scoped and budgeted. Others need a rare skill embedded in their teams over time. We do both, with the same engineers and the same level of commitment.
Fixed-scope mission
Scope, duration and deliverable defined before we start.
You know what you are buying and what it costs from day one. The risk of overrun sits with us, not with you. This is how our five packages work, from a 3-week diagnostic to full industrialisation.
The need is identifiable, the budget must be approved upfront, or you want to test our work on a bounded scope.
Embedded engineering
Our engineers work within your teams, over time.
You already have a team and a direction. What you are missing is a rare skill — Foundry engineering, putting models into production, data architecture — to carry a project that matters. We embed it with you, with the accountability that comes with it.
The work is long-running, the scope will evolve, or you want your teams to build skills by working alongside ours.
In both cases: the same engineers, the same standards. The engagement model adapts to your organisation — not the other way round.
We deploy Forward Deployed Engineers only.
You have to be where the problem is.
In the early 2000s, Palantir ran into an uncomfortable realisation: its technology was powerful, yet unusable as delivered. The hard part was not the software — it was understanding the real work of the person meant to use it. What an analyst does at 9am in front of their data, no specification has ever described.
Their answer broke with the entire industry. Instead of sending salespeople to collect requirements and then developers to build them remotely, they sent engineers to sit next to the users. Same desk, same hours, same problems. Their job was not to gather requirements: it was to live the problem, then build.
These engineers are called Forward Deployed Engineers. That model is the reason Palantir deployments hold where other programmes stall.
Twenty years on, AI has proven the model right.
An artificial intelligence system cannot be specified in advance. You do not know what a model will do well until you confront it with real data, real edge cases and real users. The specification written in month one is obsolete by month two — not through negligence, but by nature.
This is why the companies deploying AI seriously now recruit Forward Deployed Engineers. The model is no longer a Palantir peculiarity: it has become the way AI reaches production without losing two years on the way.
We did not read this in a book. We learned it in the field.
Our practice of Forward Deployed Engineering does not come from a trend report: it comes from Palantir Foundry deployments run in aerospace and reinsurance, where the method was forged. We have seen what it produces — document robots that divide errors by four, systems that hold for years — and we have seen what happens when you depart from it.
That is why we do not send junior profiles supervised from a distance, nor executors working from specifications. Every engineer we deploy is able to understand your business, talk to your operational teams and write the code that follows. It is demanding to recruit for. It is the only way we know to deliver systems that actually serve.
No gap between spec and reality
What gets built is built in front of you, on your real data. There is no moment of truth at delivery — the truth is there every week.
Decisions are made in the room
A business question gets its answer within the hour, not at the next steering committee. That is the difference between a six-month project and a two-year one.
Your teams build skills
Working next to our engineers transfers more know-how than any training course. It is built into how we work, not billed as an extra.
Bad news arrives early
If an approach does not work, you know in week two — not in month six. We would rather save you a budget than spend it.
Which model fits your situation?
Thirty minutes with an engineer to work it out. Often the answer is simpler than expected — and sometimes it is to not start anything yet.