The forward deployed engineer model for AI companies
Buyer guide
Selling AI is easy to start and hard to finish. The forward deployed engineer model puts engineers, called FDEs, inside customers until the system is in use. Here is how it works and when it pays off.
- No deposit to get started
- Every requirement read by us
- A best-fit shortlist, not a pile of CVs
- One agreement, one invoice
The idea
Close the gap between demo and production
Many enterprise AI trials never reach production. Embedding the engineers who deploy the system is one of the most direct ways to change that.
That is the bet behind the recent wave: AWS's Forward Deployed Engineering unit, Microsoft Frontier Co., and the services arms Anthropic and OpenAI set up with private equity firms and banks.
Tell us the role and the hours. We send you a shortlist of people who fit.
- No deposit to get started
- Every requirement read by us
- A best-fit shortlist, not a pile of CVs
- One agreement, one invoice
How the forward deployed engineer model works
- 01
Embed
Engineers work inside the customer, with access to their data and people. AWS uses pods of engineers.
- 02
Focus on one result
One workflow, one measure. OpenAI FDEs are measured on production adoption and measurable workflow impact.
- 03
Build in production
Real systems in the customer's environment, not proofs of concept.
- 04
Hand over
AWS says its embeds aim to leave self-sufficient teams behind.
- 05
Feed the product
Repeated custom work becomes product features, so the next deployment is cheaper.
When it pays off, and when it does not
Good fit
- High contract values that justify engineer time
- Products that need customer data and integration to work
- Early markets where every deployment teaches you something
- Regulated industries where trust is earned on site
Poor fit
- Self-serve products with small contracts
- Deployments that are already repeatable from docs
- Teams that will not feed field learning back to product
- Customers unwilling to grant access
The risks
FDE work does not scale like software. Without a loop into product, a company becomes a services business with software margins on paper only.
For buyers, deployment is only the start of what an AI system costs. Lock-in to one vendor's stack shows up later, in running costs. Ask who will run the system after the engineers leave.
For companies
Try the model on one system
Send your requirement for one system before you build a team. We send a shortlist of people who fit.
- No deposit to get started
- Every requirement read by us
- A best-fit shortlist, not a pile of CVs
- One agreement, one invoice
Tell us what you need.
Send your requirement. We send you a shortlist of senior leaders who fit. One agreement and one invoice, with the terms agreed in writing before anything starts.
- No deposit to get started
- Every requirement read by us
- A best-fit shortlist, not a pile of CVs
- One agreement, one invoice
Send your requirement
- No deposit to get started
- Every requirement read by us
- A best-fit shortlist, not a pile of CVs
- One agreement, one invoice
Apply to join
Talent never pays to apply, be shortlisted or be placed.
FAQ
Questions people ask
What is the forward deployed engineer model?
A sales and delivery approach where a vendor embeds its own engineers with a customer to get the product into production use, then feeds what it learns back into the product.
Is the FDE model the same as professional services?
Close, with differences in principle: FDEs are product engineers, and their field work is supposed to change the product. See the FDE vs consultant comparison.
Explore more
- How it works
What happens after you send your requirement, step by step.
- Fees
No deposit. One agreement and one invoice when someone starts.
- What is fractional
Senior leaders for part of the week, and when that makes sense.
- Locations
Talent based across Asia, Europe, the Middle East, Africa and the Americas.
How it works
Fees
What is fractional