FORWARD DEPLOYED ENGINEERING.

Core principles

Doing things that don’t scale — at scale

Most software companies build one product and sell the same thing to everyone — that’s what scales. The forward deployed model does the opposite: a senior engineer goes and sits inside a single customer and hand-builds exactly what they need, one account at a time. It’s slow and manual — famously, it “doesn’t scale.”

The usual startup advice is to do that kind of hands-on work only at the very start, then stop and automate it away. The forward deployed model never stops: it runs that high-touch motion on purpose, customer after customer, market after market — because being in the room is the only way to find the problem actually worth solving.

Unscalable work, run as a repeatable system — and it compounds. Every hand-built solution feeds a shared platform: a general product built backwards from what the work on the ground actually needed. So each non-scaling engagement makes the next one easier, cheaper, faster, and more impactful. The hands-on motion never disappears, but the platform beneath it grows — and that’s how doing things that don’t scale becomes doing them at scale.

Gravel road → paved superhighway

The FDE builds a “gravel road” to where the product needs to go — the simplest, roughest path that solves this one customer’s problem.

The product and engineering team’s job is to look at that gravel road and work out how it should generalise to the next five-to-ten customers, turning it into a “paved superhighway.”

The paved road usually looks different from the gravel road, because it has to serve many customers further down the road.

Building backwards (“back-propagation”)

Nobody hands you a spec — the customer doesn’t have one either. So the first move is always to find it: the use case that’s genuinely valuable, the top priority worth betting on. And that never goes away — however much platform you’ve built, however much it accelerates the work, there’s still no spec. You always have to identify the thing worth building.

Then you build backward. Make it work for that one specific case first; then ask what the general version is, and build it backward from the specific. It accumulates into one coherent platform — a growing library of accelerators, components, and adapters. Combine the right ones and they harden into reusable solutions for a vertical or a use case; in time, those can grow into products in their own right. It’s a continuous loop — solve the specific, generalise it, integrate it back, reconciling the friction each pass — building backward and inductively, always with an eye on the reusable version.

Land and expand

You start by solving one of the customer’s top problems — a real one, not the easy one. Solve it well and you earn the right to the next problem, often a more valuable one, and the one after that. The question stops being “how do we ship the same thing to every customer?” and becomes “how do we land, then expand?”

The first problem has to be one of the CEO’s top-five priorities. Anything smaller and leadership won’t have the energy for the hard part — and the hard part isn’t building the software; it’s the change and adoption needed to put something new into how the business actually runs.

Team structure & roles

Where we sit

AI Engineering & Platforms is a newly formed division inside the Office of the CTO.

FDEs report to the Head of AI Engineering & Platforms, who reports to the Chief Transformation Officer.

We start as one delta team

At the start, the whole team runs as a single delta team — a small, elite group of founding engineers playing the forward deployed role.

FDEs deploy alone or in pairs to accounts, and own solution design all the way to production.

Pods form as we specialise

Over the following months, small pods form — each a specialisation around an industry, a set of use cases, or a product. A pod per vertical.

How you grow

As deployments accumulate, the platform compounds: each one makes the next better, faster, and more impactful.

As FDEs accumulate battle scars, platform fluency, and domain knowledge, they earn a place in the product, platform, and industry pods.

From there the path forks — pod lead, for those with an aptitude for people leadership; principal and distinguished engineer, for those who keep growing on the technical track.

The teams around us

The AI engineering team works in partnership with — and is backed by — the account, solutioning, strategy, and change teams.

Deployment strategists and account executives are the commercial and strategic counterweight: they own change management, “seeing around corners,” and navigating large, multi-division accounts.

Forward deployed is a verb

It’s less about the forward deployed engineer and more about the verb — forward deployed engineering. We’re committing to building the product this way.

Cadences

The customer rhythm

The sync cadence runs per use case — an account might have several use cases in exploration at once. A new one opens with daily syncs through discovery, holding them until it reaches cruising speed; they then ease to weekly syncs, and tighten back to daily as it approaches production.

Each pilot also books two reviews up front, straight into calendars — a mid-point review and a final one, each with a fixed date and time so they don’t quietly slip. The final review brings in all the customer’s stakeholders, wrapped up in person.

Beyond the pilot, quarterly business reviews add structure — a few minutes on what’s been done, most of the time on what’s next. Useful later, not an early-stage priority.

And it all runs on presence — being on site is what “forward deployed” means. An FDE is flown in about a week at a time, a few times a quarter per account, more around a go-live, and on site the night before a launch. Presence is what speeds up the deployment, the relationship, and the next opportunity.

The team rhythm

Weekly, the platform compounds: a standing sync between the field and the platform team. Thursday, FDEs write up the week — notes, blockers, and feature requests — and submit them asynchronously; Friday, a live session works through the biggest issues, and the platform team decides what gets built into the platform.

Monthly, the team steps back to the numbers — is the model compounding? Platform share of each deployment, outcomes delivered per account, and the trajectory of each engagement.

Quarterly, the horizon lifts to strategy: which verticals are real enough to form a pod around, what’s worth hardening into a reusable solution or product, and where to land and expand next — plus an honest retro on the model itself.