Embed
Forward Deployed
Engineers
One senior engineer, embedded with your users and your data, who owns whether the system works — not a ticket queue. The model AI companies use to get software working inside real businesses, available without hiring one.
When you need one
The code is not the problem.
Everything around it is.
The demo works, production doesn't
An AI feature that handled the test cases falls over on your real data, your real volumes, and the edge cases nobody listed.
It's live, but nobody uses it
A platform you bought or built is switched on, and the team still works in spreadsheets. Adoption is the project now.
Requirements only exist in people's heads
Nobody can write the spec from the outside. Someone has to sit with the people doing the work and find out.
Three teams, no owner
The work crosses departments and every team owns a piece. An FDE owns the outcome across all of them.
Legacy systems in the way
The ERP, the undocumented API, the database only one person understands. Integration is where these projects stall.
Your engineers keep getting pulled in
One integration keeps dragging your best people off the roadmap. An FDE takes it off their plate and finishes it.
How an engagement runs
Built to finish,
not to renew.
Weeks 1–2: map
Architecture, data, workflows and the people involved. We say out loud what is really in the way.
Agree the outcome
Milestones set at the outcome level — "invoices processed without re-keying" — not as a list of tickets.
Build inside your systems
Production code in your repo: integrations, pipelines, agent orchestration, the unglamorous glue.
Adoption & handover
We stay until it is in daily use, then hand over documentation, tests and a team that can run it.
Choosing a model
FDE, consultant, or new hire?
Three common ways to get an AI or integration project working. They differ in who is accountable when it reaches production.
| Option | What you get | Accountable for | Trade-off |
|---|---|---|---|
| Forward deployed engineer | A senior engineer embedded with your users and data | The system working in live use | Costs more per person than augmentation; knowledge sits with one or two people until handover |
| AI consultancy | Strategy, use-case selection, a report or a pilot | The recommendation | Implementation and integration are left to someone else |
| Full-time hire | A permanent senior engineer on your team | Whatever you assign | Months to hire, and the role may outlast the project |
If you already know exactly what to build and simply need more hands, you do not need an FDE — team extension is the cheaper model. We will tell you which fits.
Why one engineer is enough now.
The role was popularized at Palantir and became standard at AI companies such as OpenAI, Anthropic and AWS, because on a real rollout the hard part is rarely the model. It is the messy data, the integration nobody documented, and the workflow that exists only in someone's head.
With coding agents doing much of the typing, one senior engineer who understands the business can now deliver what used to need a small team. What cannot be automated is judgement, context and accountability — which is exactly what an FDE brings.
Our FDEs are senior engineers with 5+ years of production experience, backed by our CTO's twenty years of building systems. See how we build with AI.
Common questions