What is a forward deployed engineer?

Where the title comes from

In many software companies, some people build the product and others sell or support it. The forward deployed engineer sits in a different place: next to the customer, writing code against the customer's real problem. Palantir is the company most associated with the title. In its own engineering blog, a Palantir forward deployed software engineer describes the job as joining the first conversations with a customer, helping scope the business problem, deciding how to deploy the product against it, and being there with the users when the application goes live.

That description is the useful part, and it travels well beyond one company. The word "forward" means close to where the work happens. The word "deployed" means the engineer is placed there for the length of the engagement. The word "engineer" means the output is working software, not a slide deck.

For a small business the same idea applies at a smaller scale. Instead of a vendor placing an engineer at a large customer, an engineering studio places one inside your team for a short, defined stretch, with a defined result.

What a forward deployed engineer actually does

The day-to-day work follows a loop that repeats until the system is live:

• Listens to the people who do the job. Managers describe how a process is supposed to run. The people doing it describe how it actually runs. We look for that gap: it is where automation opportunities show up.
• Maps the workflow. Where does a request come in, who touches it, where does it wait, and what gets retyped from one system into another?
• Builds against real data. A prototype that works on invented examples proves little. An FDE connects to a sample of your real documents, your inbox or your records and shows the idea working on them.
• Ships and stays accountable. The engineer is still there when the first real users try it, and fixes what breaks.

None of this requires a particular technology. AI work is one example: an agent that drafts replies, a copilot that answers from your own documents, an assistant that fills in a form. The role matters more, not less, for AI work, because a language model is only useful once it is connected to your tools, your data and your approval steps, and that connection is specific to your business.

How an FDE differs from a consultant, a solutions engineer and staff augmentation

The titles overlap in conversation, so it helps to compare what you actually receive.

The practical difference shows up when the answer is "it depends." A consultant can tell you what to try. A solutions engineer will show you how the vendor's product could do it. Staff augmentation gives you capacity but expects you to know what to build. A forward deployed engineer is the role that finds the problem, picks the approach that fits and builds it, and the same person is there when it meets reality.

What a 1–2 week Discovery Sprint produces

At NEXIRT, a custom forward deployed engagement is designed to start with a paid Discovery Sprint of one to two weeks. The sprint is designed to follow one shape and to scale to the size of the workflow. On a medium workflow over two weeks, a Discovery Sprint is designed to run like this:

1. Days 1 and 2: interviews. Working sessions with the people doing the job, not just the people managing it.
2. Days 3 and 4: the workflow map. A visual map of how the work moves today, with the bottlenecks marked.
3. Days 5 and 6: scoring. Each place AI or automation could help is scored on how often it happens, how much time it takes, how costly a wrong answer would be and whether the data exists. The top one is chosen with you.
4. Days 7 to 9: a working prototype. The chosen idea runs against a sample of your own data, so you see it behave before you commit to building it.
5. Day 10: the roadmap and the quote. A written roadmap and a fixed-price quote for the build. The sprint fee counts toward the build if you go ahead.

A one-week sprint compresses the same steps for a smaller workflow. Either way you leave with four things: a workflow map, an AI-opportunity scorecard, a working prototype and a fixed-price roadmap. They are yours whether or not you build with us. If you do go ahead, NEXIRT's published delivery range for a custom build is 6–16 weeks, with weekly previews along the way, and you keep the source code, the infrastructure code and the documentation.

When a small business needs a forward deployed engineer

The role pays for itself in a specific set of situations. You likely need one when:

• The work is specific to how your business runs, and every off-the-shelf tool you tried fits most of the work and fights you on the rest.
• A person on your team spends hours a week moving information between systems by hand, and nobody has time to fix it.
• You want AI in a workflow but cannot tell which task to start with, or you tried a chatbot and nobody used it because it could not actually do anything.
• The data you would need is spread across an inbox, a shared drive and a couple of tools, and the answer to "what is the status of X" takes a half-day of searching.
• You need a working version in front of your own staff quickly, before committing to a long build.

When you do not need one

Be skeptical of any vendor, including us, who says every business needs custom work. Anthropic's engineering team advises finding the simplest solution possible and adding complexity only when needed. The same logic applies to hiring. You probably do not need a forward deployed engineer when:

• A software product you can already buy does the job. Buy it, configure it, and spend the money elsewhere.
• The task is general-purpose, such as drafting a first pass of an email or summarizing a public article. A general AI assistant is enough.
• Nobody owns the workflow. An engineer can build a system, but not decide how your team should run.
• The process is still changing every week. Stabilize it first; automating a moving target wastes money.

When not to hire NEXIRT

We would rather say this up front. NEXIRT is the wrong fit when:

• You need a product configured, not a system built. If the answer is "set up this software," a reseller or the vendor's own implementation team is cheaper and closer to the product.
• You want to rent developers for an open-ended backlog. That is staff augmentation. We work on defined results with a fixed-price quote, not on open-ended hourly capacity.
• You need a formal certification or audit attestation. We build to sound practices and describe plainly how we handle security, but we do not claim certifications. The security page lists what we do and what we do not claim.
• Nobody on your side can give us interview time. The sprint depends on the people who do the work. Without them the map and the prototype are guesses.
• You want an AI agent to act on your customers with no person approving it. In the systems we build, what an agent drafts for your customers waits for a person to approve it.

Questions to ask any vendor before you start

Whether you hire us or someone else, these questions separate a real forward deployed engagement from a rebranded consulting project. They follow the four functions of the voluntary NIST AI Risk Management Framework: govern, map, measure and manage.

Govern: who is accountable?

1. Who is the named engineer, and who on my side approves what the system does?
2. Where does my data go, who can see it, and can the system run in my own cloud account?

Map: do you understand my context?

3. Which people will you interview, and will it include the ones who do the job every day?
4. What happens if the prototype shows the idea is not worth building? Will you say so?

Measure: how will we know it works?

5. What will we measure, and what does a good result look like before the build starts?
6. Do I get a working prototype on my own data before I commit to the full build?

Manage: what happens when it goes wrong?

7. Is there an audit log of what the system did, and a person who signs off on what is sent to a customer?
8. Do I own the source code, the infrastructure code and the documentation at the end?

A vendor who answers these concretely is worth a conversation. A vendor who answers with a slide about "transformation" is not.

How to start

If you run a business in Las Vegas, Henderson, Summerlin or North Las Vegas, the Discovery Sprint is on-site. Elsewhere it runs remotely. The first step is a short conversation: tell us the workflow that eats your team's week and an engineer replies within one business day. Read how the Discovery Sprint works, see what working with an AI consultant in Las Vegas looks like, or read the companion guide on AI integration for small business.