Your AI pilot finally becomes how the work gets done.
Forward deployed engineers (FDEs) sit with your operators, work in your systems and write production code against your real data — until the AI or software you bought actually changes how the work gets done.
Most enterprise AI never leaves the pilot.
The demo works on clean sample data; then it meets the real estate — a twenty-year-old core system, a spreadsheet that runs a department, an approval nobody wrote down, a regulator who wants to know why the model said what it said. The gap between a promising demo and something people rely on every day is not a model problem. It is an engineering problem that can only be solved from the inside.
A forward deployed engineer closes that gap by being there. The model was pioneered by Palantir and is now how the leading AI companies get their products into production at large customers: an engineer who works with one customer across many problems, instead of one feature across many customers. They sit with the people who do the work, learn the workflow first-hand, build in your environment, and stay until the result holds up. We bring the same model to your organisation — with AI-native engineers who have shipped and operated enterprise platforms of their own.
The test is simple: if the people who were supposed to use it still do the job the old way, we haven’t finished. Adoption and measured outcome are the deliverable — code is how we get there.
Four things a forward deployed team does that nobody else will.
A team is usually a lead FDE plus one or two engineers, working on site or alongside your teams for part of every week, with the rest of our engineering practice behind them.
Learn the work where it happens
Before anything is built, the team shadows the operators, analysts and approvers who will live with the result. We map the real workflow — including the workarounds — and pick the one problem where software can move a number the business already tracks.
Build in your environment
Production code in your repositories, against your data, inside your security boundary. Integrations with the core systems that matter, not a sidecar that only works in a demo tenant. Your engineers review every change and can run it without us.
Make AI hold up in production
Retrieval over your own documents, model selection, evaluation suites built from your real cases, guardrails and audit trails. A model goes live only when it beats the agreed accuracy bar on your data — and the evaluation keeps running after launch.
Prove it, then scale or hand over
Every engagement is tied to an outcome agreed up front — hours saved, cases closed, errors avoided. When it is proven, we either scale it to the next team or hand it over with runbooks, tests and the people who built it available to yours.
Not a consultancy. Not extra headcount.
Forward deployed engineering sits between the people who advise and the people who take tickets: senior enough to define the problem, close enough to the work to fix it.
Choose forward deployed engineering when you have a real operational problem, a sponsor who owns the outcome, and AI or software initiatives that look good in pilots but are not changing how the work gets done.
Look elsewhere if you need a fixed-scope build against a finished specification (see custom software), or more hands for a team that already knows exactly what to build (see staff augmentation).
How an FDE engagement runs.
Embed
The team joins your teams, gets access, shadows the work and maps the systems involved. Together with the business owner we pick one problem and the number it should move.
Build
Working software in your environment, released to a small group of real users early and improved on their feedback. Integrations, data pipelines and AI components built to production standard from the start.
Prove
The result is measured against the baseline agreed in phase one. If it does not move the number, we say so and change course — or stop.
Scale or hand over
Proven solutions roll out to the next team or site. Your engineers take ownership with tests, runbooks and documentation, and the team moves to the next problem — or steps back.
- Production software running in your environment, in your repositories
- Evaluation suites and monitoring for every AI component
- Integration and data pipelines documented as code
- A measured result against the baseline agreed at the start
- Runbooks, architecture notes and a team trained to own it
Inside your systems early, not after a long discovery.
We keep the opening short on purpose. Access, a walk-through of the workflow and a first scoped problem come first; a working slice in front of real users follows as early as possible. Discovery happens while building, because the fastest way to learn what a workflow really needs is to put something in it.
- Access, security review and data boundaries agreed before a line of code is written
- Shadowing sessions with the people who will use the result, not only their managers
- A first working slice in users’ hands early — then iterate on what they actually do with it
Shadow
We sit with the people who do the work and watch how it is done today, including the spreadsheets, workarounds and manual steps nobody has written down.
You get: A written picture of the current process.
Map
We map the process, the systems it touches and where the data flows, then pick the point that costs the most time or money.
You get: One measurable goal and an agreed scope.
Build
We write the code in your repositories and test it on your real data, inside your security boundary. Nothing that only works in a demo.
You get: Working software in your own environment.
Ship
We roll it out with your team step by step, train the people who will use it and hand over the runbooks.
You get: A system in daily use, with documentation your team owns.
Measure
We measure the goal agreed in step two. When it is met, your engineers take over, or the team moves on to the next problem.
You get: A before-and-after comparison against the goal.
AI that has to pass your test, not ours.
The usual way an AI pilot dies is silently: it is impressive in week two and quietly abandoned in month three because nobody trusts it. We build the trust in. Evaluation sets come from your real historical cases, the acceptance bar is agreed with the business owner, and the system explains where its answers came from.
- Evaluation suites built from your own cases, rerun on every change
- Human review on decisions that carry risk — by design, not as an afterthought
- Personal data handled to KVKK and GDPR, with self-hosted models where the contract requires
Problems that only get solved from the inside.
AI assistants on your own knowledge
Policies, contracts, product manuals and case history made answerable — with sources, permissions and an audit trail your compliance team accepts.
Back-office workflow automation
Claims, approvals, reconciliations and document-heavy processes where people re-key the same data between systems every day.
Getting a platform truly adopted
Loyalty, sales-performance or transport platforms — ours or someone else’s — wired into the messy estate around them so teams actually use them.
Decisions from data you already have
Pipelines and models that turn operational data into a daily decision: which customer to call, which shipment is at risk, which claim to check.
Legacy systems that can’t be replaced yet
Integrations and safe automation around core systems that will be with you for years — without waiting for the big modernisation programme.
Regulated environments
Banking, insurance, healthcare and public-sector work where data residency, KVKK/GDPR and explainability decide what is allowed to ship.
Frequently asked questions
Services that pair with this.
Have a problem that keeps surviving pilots?
A 30-minute call with an AI-native engineer. Bring one workflow that should be working better — we’ll tell you honestly whether an FDE team is the right way to fix it.