Forward deployed engineering: the model that ships AI into real operations.
A forward deployed engineer embeds inside an operation to design, build, and run AI in real workflows, and stays accountable for whether it works. Not a deck. A running system. Bragi brings the model to mid-market companies, with senior ownership of the result from first contact to a measured outcome.
What is a forward deployed engineer?
A forward deployed engineer (FDE) is a senior engineer who works directly inside a client environment rather than from a vendor office. They write production code, build the integrations and workflows on top of existing systems, and unblock the messy realities: data that does not match the docs, processes nobody mapped, edge cases the demo never hit.
The role started at Palantir, spread to companies like OpenAI and Clay, and has been called one of the hottest jobs in tech. The reason is simple. The gap between an AI capability and a result has turned out to be the whole problem, and the FDE is the person who closes it.
Advice is cheap now. The system is the work.
For two years the question was whether AI could do the task. Now it can. The question that decides whether it is worth anything is whether it runs in your operation and survives contact with real data.
That is where most AI work dies. The strategy was sound. The pilot demoed well. Then nobody owned the build, the internal team had ten other priorities, and six months later the project is a screenshot in a board pack.
Forward deployed engineering exists to remove that failure mode. The person who tells you what to build also builds it, deploys it where the work actually happens, and answers for the outcome. Any model will write you a strategy. The value is in the system that runs after the advice.
Forward deployed engineering for the mid-market.
Most FDE coverage is about frontier labs and the Fortune 500, companies that can hire a bench of forward deployed engineers outright. Mid-market companies, roughly $20M to $500M in revenue, have the same problem and none of that capacity. They are adopting AI across functions, the spend is real, and there is no one whose job is to turn it into a working operation.
That is the gap Bragi works in: the forward deployed model brought to mid-market operators. One senior operator who finds the AI work worth doing, builds the one or two workflows where the commercial case is clear, deploys them into the real operation, measures the result, and transfers the capability to the team so it keeps running without us.
Different from consulting and AI agencies.
Forward deployed engineering combines the two halves that usually live apart: the judgment to choose the right work, and the engineering to ship it, under one person who owns the outcome from first contact to a measured result.
| Dimension | Consulting firm | AI agency | Forward deployed |
|---|---|---|---|
| What you get | Strategy deck and handover | AI tool deployments | A running system in your operation |
| When they leave | At the deck, before anything works | When the tools are stacked | After the result is measured and transferred |
| Who owns the outcome | No one after handover | No one, dependency grows | One operator, first contact to result |
| Starting point | Slide-led discovery | Tool-led, no baseline | Scored baseline of where AI pays |
| What scales down | Engagement extends and grows | Stays the same scope | Designed to shrink as capability transfers |
What a forward deployed engagement looks like.
It starts with a scored baseline of where AI can actually move the business, then a single high-impact workflow built, tested, and deployed into real operations, scoped to prove commercial value before expanding.
Scored baseline
Where AI can actually move the business, scored across revenue, cost, speed, risk, and capability. The starting point is evidence, not a vendor wish list.
One workflow, shipped
A single high-impact workflow built, tested, and deployed into the real operation in six to ten weeks. Scoped to prove commercial value before expanding.
Acceptance criteria up front
Measurable acceptance criteria agreed before the build begins. The result is judged against numbers set in advance, not a demo that looks good in a meeting.
Capability transfer
The capability is transferred to the client team so the work keeps running without us. The engagement is designed to make itself smaller over time.
Who it is for.
Mid-market operators, founders, COOs, and CFOs, who are past asking whether to use AI and are now asking what is worth building and who will own it. It fits best where there is a real operation to improve, a budget already going to AI, and no one inside whose job is the result.
Mid-market operators in the $20M to $500M revenue range. The AI spend is real, it sits across functions, and no one inside has the result as their job.
Founders, COOs, and CFOs who are past asking whether to use AI. The live question is what is worth building and who will own it.
Companies with a real operation to improve. The model needs workflows that matter commercially, not a greenfield experiment.
Teams with budget already going to AI but no internal capacity to turn it into a running operation that survives contact with real data.
Questions leaders ask first.
What is a forward deployed engineer?
A senior engineer who embeds in a client operation to design, build, and run AI in their real workflows, and stays accountable for whether it works, rather than handing over a recommendation and leaving. The role started at Palantir, spread to companies like OpenAI and Clay, and has been called one of the hottest jobs in tech because the gap between an AI capability and a result has turned out to be the whole problem.
How is forward deployed engineering different from a consultant?
A consultant delivers a strategy and leaves before anything is running. A forward deployed engineer builds and ships the system the strategy describes, in your real operation, and owns the outcome. Advice is cheap now. Any model will write you a strategy. The value is in the system that runs after the advice.
How is it different from an AI agency?
Agencies optimise for output volume and tend to create dependency, stacking tools without a baseline. Forward deployed engineering starts from a scored baseline, builds only where the commercial case is clear, and transfers the capability so the work continues without the provider.
Does forward deployed engineering work for mid-market companies?
Yes. The model originated at large tech companies, but the need is sharper in the mid-market, where the AI spend is real but there is no internal capacity to turn it into a running operation. Bragi brings the model to companies in the $20M to $500M revenue range, with one senior operator rather than a bench of engineers.
What does a forward deployed engagement deliver?
A scored baseline of where AI can move the business, then one or two high-impact workflows built and deployed into real operations in six to ten weeks, with measurable acceptance criteria agreed before the build and capability transfer to the client team so the work continues independently.
Start with a scored baseline.
The four-week BRAGI Assessment produces a baseline, an opportunity map, and a recommendation for what to build first. It is where most forward deployed engagements start.
Or talk through a build directly. For the operating-leadership side of the work, see the fractional Chief AI Officer engagement.