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Forward deployed engineering
Forward deployed engineers, inside your team
A forward deployed engineer is a software engineer who works inside a customer’s team, on that customer’s systems, until a capability runs in their daily work. For most businesses today that capability is AI. predeploy rents you forward deployed engineers to get AI working before you scale it, and helps you decide when to hire your own.
What an FDE is
Engineers who work where the problem is
Forward deployed engineering puts an engineer next to the people doing the work, rather than behind a product roadmap. They learn the process first-hand, build against real data and stay until the thing runs.
The role grew up in companies that sell complex software into large organisations, where the gap between what the product can do and what the customer gets from it is closed by engineers on the customer’s side. AI has the same gap, which is why the role now turns up far beyond those companies.
That is also where our name comes from. predeploy deploys engineers before you scale, so what you scale already works.
A consultant tells you what to build. A product engineer builds it for everyone. A forward deployed engineer builds it for you, in your systems, and hands it to your team.
The model
Forward-deployed, or back at base
Flip the switch to compare an engineer working on a vendor’s roadmap with one working inside your team.
The bars compare approach, not measured results.
Why now
Demand for AI engineers outruns supply
The UK AI sector has grown fast, and the people who can build with AI are in short supply. Renting an engineer is one way round the queue.
The DSIT, Artificial Intelligence sector study 2024 counted 5,862 AI companies in the UK in 2024, up 58% in a year. Among organisations in the AI labour market, the DSIT, AI Labour Market Survey 2025 found that 57% report a technical skills gap.
Source: DSIT, Artificial Intelligence sector study 2024 (Sep 2025)
Source: DSIT, Artificial Intelligence sector study 2024 (Sep 2025)
Source: DSIT, AI Labour Market Survey 2025 (Jan 2026)
When you need one
Signs it is time for an FDE
You do not need a forward deployed engineer to try AI. You need one when trying has stopped turning into anything that runs.
- Pilots that never ship. Proofs of concept work in a demo and stall before production.
- Tools nobody uses. Licences were bought, but the work happens the old way.
- No one owns it. IT is busy, the business has no engineers and the vendor has moved on.
- Data in many places. The useful information sits across the CRM, inboxes, files and spreadsheets.
- A deadline. The board wants AI working this year, not a plan for next year.
Hire or rent
Hire one, or rent one
Tick what applies and the needle shows which route fits. The verdict is a starting point for a conversation, not a rule.
Tick what applies
The needle moves with each answer.
Renting through us suits a business that needs AI working now. You get engineers who have done the work before, without a search, a notice period or a permanent headcount decision made too early.
Hiring suits work that is permanent and core to the product. If you want to hire permanently, or the real gap is a leader, our sister company Fractional Quest is a fractional recruitment agency. It can place a fractional CTO to own the technical roadmap, or a fractional COO when the operating model has to change around the new workflows.
Engineers and technical leaders looking for this kind of work can browse open fractional jobs on Fractional Quest.
Time to start
Building before a hire would start
A permanent hire takes a search and, usually, a notice period before any work begins. A rented engineer starts on the work.
Illustrative plan, not measured data. Search and notice periods vary widely; we give you a real start date after the scoping call.
How we work
Assess, build, train, hand over
Our forward deployed engineers follow the same four stages on every engagement. Each one ends with something your team can use.
Assess
Learn the work first-hand
The engineer sits with the people who do the job, maps the process and finds the steps AI can take over safely.
Build
Ship against real systems
They build inside your stack, with your data, in short loops your team can see and test.
Train
Teach the people who use it
Users learn the workflow; someone on your side learns to maintain and extend it.
Hand over
Step back, on purpose
Documentation, named owners and a list of what to build next. We leave when your team can run it.
FAQ