What forward-deployed engineering is
The question I am asked most: what is forward-deployed engineering, and how is it different from the consulting and training you already buy? This page is my working answer and the framework I use in every engagement and cohort.
What it is
Forward-deployed engineering (FDE) is a way of delivering technology in which engineers work inside the customer's environment, close to the problem, instead of handing over a product from a distance. For AI, it means finding the real workflow, working within the client's data and systems, and staying until the result runs.
An FDE owns an outcome, not a backlog. The work is done when the system runs in the customer's hands and someone there can keep it running.
Palantir popularised the model, and the large AI labs now run forward-deployed teams of their own, because a capable model is not the same as a working system in a specific company.
What it is not
- Not pre-sales. A solutions engineer helps win the deal. An FDE owns delivery after it.
- Not consulting that leaves a deck. The output is working software, recorded decisions and a trained owner, not a recommendation.
- Not staff augmentation. An FDE is accountable for the result, not for hours.
- Not a bootcamp. Tool knowledge is the entry ticket. The job is judgement inside someone else's organisation.
Two things an FDE must have
Most engineers I meet have one of the two. The programmes I run exist to build the other.
Technical depth
The ability to write production code, build evaluation harnesses, configure retrieval and agents, and integrate securely with legacy systems.
Customer judgement
The discipline to clarify fuzzy requirements, write down what is out of scope, work within organisational constraints, and make sure an operational owner can run the system after launch.
How an engagement starts
Before any technology choice, I sit with the people who own and do the work. In a readiness review that is up to five interviews over two weeks. The questions are always the same.
- What is the task, and who owns it today? Not the department. The person.
- What does useful look like? The evidence that would justify changing the workflow.
- What can the system reach? Data, access, permissions and the systems the output must land in.
- What must not happen? The failure that would end the project, and the review step that prevents it.
- Who runs it afterwards? If there is no answer, the work is not ready to start.
The result is a discovery brief: scope, workflows, data boundaries and non-goals on one or two pages. Everything later is tested against it.
The problems I am called into
The pilot works, but nobody knows how it fits the operating workflow
The demonstration runs on a laptop. Nobody has decided who owns the output, where it appears, or what happens when it is wrong. The work here is ownership, system boundaries, evaluation and the conditions for deployment.
The architecture debate begins before the business decision is clear
Teams argue about models, vector stores and frameworks before agreeing what outcome is wanted and what evidence would prove it. The work here is to establish the outcome and constraints first, then choose.
AI prototypes cannot cross enterprise data and integration boundaries
The prototype used an export. The real system needs access controls, legacy connectors, audit and a security review. The work here is retrieval, agent and API design around real constraints.
Engineers know the tools but cannot yet own customer-context delivery
The team can build an agent in a sandbox. It cannot yet sit with a client, find the real problem and ship into their systems. The work here is capability: realistic scenarios, applied labs, architecture defence and handover.
The four gates
I assess forward-deployed work, mine and my participants', against four gates. Each gate has a written output and a rubric. Passing a gate means the output holds up to questioning, not that a demonstration ran.
G1. Context and problem framing
The discovery brief. The task, its owner, the evidence that matters, the data that can be reached and what is out of scope.
G2. Architecture
An architecture decision record. Components, data and integration boundaries, the model and retrieval decisions with the reasoning written down, and the security perimeter.
G3. Integration
A working build connected to real systems, not a mock. The evaluation harness runs against agreed test cases.
G4. Defend and hand over
The engineer explains the decisions to people who did not build it, shows the evaluation results, and hands a runbook to a named owner.
What a cohort produces
Every participant in an FDE cohort leaves with five things, each assessed against the gates above.
- A discovery brief (G1).
- An architecture decision record (G2).
- A working build inside a realistic client environment (G3).
- An evaluation report with results against agreed tests (G3).
- A handover note a named owner can run from (G4).
I report what each person demonstrated at each gate. That is not the same as who attended.
How it compares
- Deliverable. Traditional advisory may end with a recommendation. Forward-deployed delivery continues to a running system and the recorded decisions behind it.
- Accountability. Advisory usually ends at the handoff. FDE ends when the system runs reliably and your team can own it.
- Discovery. Advisory interviews and synthesises. An FDE interviews, then builds alongside your team and finds the real problems by meeting them.
- Knowledge transfer. Not a presentation at the end. Your engineers watch the decisions being made and make some of them.
- Measure of success. Not the quality of the recommendation. Whether the system works in production.
Short definitions of these and other terms are in the glossary.
Where to go next
If you are an enterprise with a stalled pilot, start with the AI Delivery Readiness Review. If you are a services firm or capability centre building this capability, see the FDE cohort. If you are deciding whether to create a forward-deployed team at all, the checklist for companies works through the sixteen questions I ask first.
Email: hello@mayurpatil.ai