AI Engineer World's Fair 2026
Forward Deployed Engineering 101
Read the talk
Forward Deployed Engineering: Turning a Platform into a Customer Outcome
Forward deployed engineering connects complex platforms to business outcomes, but its economics depend on shared primitives and a clear boundary between customer work and product development.
From a talk by Kevin Bai
What does organized data do for the business?
What does a customer actually gain from buying a powerful platform? That is the practical question behind forward deployed engineering as a go-to-market motion. Kevin Bai approaches it from work at Anthropic’s applied AI team, where he introduces himself as a member of technical staff; before that, he was the first person on Rippling’s FDE team, and earlier worked at Palantir. Bai says Rippling’s FDE team grew to around 25 people in one year. His starting point is why Palantir adopted the function and when another business might need it.
Consider Palantir’s Foundry. It centralizes an organization’s data, creates an ontology, and provides a platform for building applications. Bai introduces the ontology through a warehouse example: instead of disconnected tables, the business gets a single source of truth for its warehouses. Data acquires business meaning—proper nouns that people can work with. This is an introductory analogy; the Ontology encompasses business objects, relationships, actions, and functions, rather than simply consolidated tables.
An industry buyer can still reasonably ask what this organization of data does for the business. The platform’s success depends on the customer’s ability to use it, and that creates an adoption cost beyond the purchase price: employees must learn the platform before they can build useful applications. The vendor has sold a capability, but realizing its value remains the customer’s problem.
FDE combines software and engineering services into a purchased outcome. Engineers learn the customer’s business and build a solution on Foundry. The customer is buying neither software alone nor simply someone’s time. A consumer packaged goods company, for example, might want more shelf placement or higher sales throughput. How its data is organized matters as an implementation detail supporting those results.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Match the product’s complexity to the buyer
Bai frames the adoption problem as a two-axis grid, using a Punnett-square analogy: what are you selling, and who is buying it? Complexity alone does not require FDE. What matters is whether the customer can absorb that complexity and turn the product into something useful.
| Offering | Buyer or user | How complexity is handled |
|---|---|---|
| GitHub or Datadog | CTOs, CIOs, software engineers | Technical users learn and operate the software |
| Rippling, Jira, or Slack | Nontechnical buyers and users | Users configure the tool |
| A platform requiring custom development | Nontechnical business buyer | FDEs build the customer’s solution |
The second row reflects how Bai positions these products in this comparison: configurable tools, rather than platforms the customer must develop on. His criterion for FDE is the third case—a technically demanding offering that must reach a nontechnical buyer.
That mismatch helps explain Palantir’s market. Google, Meta, and AI labs have engineers who can build internal applications. A Fortune 500 oil-and-gas company may lack comparable software engineering depth; its pipelines move physical materials, not data. Selling it an application platform leaves a substantial gap between purchase and use.
FDE fills that gap by supplying engineers whom the customer does not need to recruit, hire, manage, or retain. They already understand the platform and work closely with the customer to discover and solve its problem. Bai compares this attention to a waiter at a fine-dining restaurant: understanding what the customer needs is part of delivering the service. Combined with the platform, that became Palantir’s way of selling to the global Fortune 500.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
The commercial case for delivering outcomes
To illustrate the commercial appeal, Bai compares customer spending across what he describes as public SaaS companies in the Fortune 500. He calls the measure ACV, meaning average contract value here; it should not be confused with annual contract value, another common use of the abbreviation.
| Company or group | Average contract value in Bai’s account |
|---|---|
| Palantir | $4 million, as of his last check |
| ServiceNow | $1.2 million |
| Workday | $600,000, recalled tentatively |
| All remaining public SaaS companies | None above $500,000 |
These are Bai’s reported figures, without a specified measurement date, calculation, or independently established ranking. They illustrate his case for the model’s commercial success rather than establish a like-for-like financial benchmark. He also points to Palantir’s high valuation relative to a workforce of a few thousand people, without supplying a valuation figure.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Two gates before building an FDE function
Applying the model starts with necessity, not enthusiasm for a fashionable role. Bai proposes two gates:
- Does the business need this go-to-market motion? Identify a situation where a technically complicated offering must be sold to a nontechnical buyer. If that mismatch is absent, FDE is probably the wrong fit. Developer relations and developer engagement can serve technical buyers; a sales-led motion can serve more traditional SaaS.
- Is there a platform, or a commitment to build one? Engineers need shared primitives beneath their customer work. The prospect of engineers directly generating revenue does not remove the cost of supporting what they build.
Even a robust platform leaves a substantial maintenance burden. The platform is a prerequisite for managing that burden, not a promise that it disappears.
AI makes the question more widespread. Speaking in a 2026 context, Bai contrasts the present with Palantir’s early market years, which he tentatively places around 2004 or 2005. Code and sophisticated custom software have become easier to produce, including agents for insurance, legal work, and other domains. His hypothesis is that software’s business model is changing: as platforms become agentic, they become more customizable, and more customers face a product whose possibilities they do not understand or know how to implement.
That creates a familiar adoption risk. If the vendor leaves success entirely to the customer’s implementation ability, moving upmarket or expanding into new horizontal or vertical markets becomes harder. Easier software construction does not automatically give customers the context or engineering capacity to realize its value. The subsequent audience questions turn to how an FDE function should operate; Bai excludes his current work from that discussion.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
How much should a primitive already do?
The first implementation question is how atomic the shared primitives should be. For a platform involving data models, Bai suggests starting by avoiding the need to define a data model from scratch. Beyond that, the right level of reuse depends on the use case and customer base.
- Substantial application foundations: In some industries, Bai suggests an application might be 60% prebuilt and 40% customized. The split illustrates a possible arrangement, not a prescribed target.
- Granular building blocks: Other industries and use cases require fine-grained configuration and tooling, so a nearly finished application would constrain the work too much.
AWS provides the analogy for broad primitives. An engineering team could buy server racks, bring them online, and maintain them, but a platform takes that work off its hands. Within AWS, DynamoDB supplies a database capability the team does not need to invent itself. AWS serves a very broad customer base, which explains the usefulness of general building blocks. A narrower customer base may justify primitives that encode much more of the application.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Share knowledge and clarify the working relationship
An audience question about collaboration initially draws an answer about having multiple FDEs on one project. Bai encourages that pattern because customer work should not depend on a single person holding all the context. If that engineer goes on vacation, the rest of the team still needs to understand and support the solution.
The questioner then clarifies that the engineers come from different companies—for example, AWS and Palantir working on the same project. After distinguishing a competitive bake-off from a partnership, Bai confirms that cross-company collaboration exists. His analogy is a contractor relationship: establish who occupies the contractor role. The answer supplies a way to think about the relationship, rather than a detailed process for managing friction between vendors.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Let field work inform the platform
Where should a requested change live: in the platform or in the customer’s implementation? Bai draws the boundary around generalizability.
| Change | Long-term home |
|---|---|
| Bespoke behavior unique to one customer | That customer’s implementation |
| A capability useful beyond one customer | The shared platform |
An early FDE program may begin with relatively few primitives. Field work then becomes a way to discover what else the product should support. FDEs scout ahead: they encounter customer problems, identify capabilities that generalize, and help reveal additional products and services the business can build. Generalization is a long-term direction, not a requirement to put every customer request into the platform immediately.
The final hiring question brings the function back to the person doing the work. Bai’s definition is a “customer-facing software engineer.” The candidate must meet the team’s software engineering hiring bar, and must also be someone the company trusts in front of a customer. Both requirements matter: the role has to understand the customer well enough to discover the right problem and have the engineering ability to build its solution.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Resources
From the talk
Explains how business objects, relationships, actions and functions connect enterprise data to operational applications.
Overview and getting-started guidance for AWS's managed NoSQL database, the reusable infrastructure example in the Q&A.
Further reading
Kevin Bai's companion primer on the FDE role, when to deploy it, and its role in enterprise growth.
Read the complete timestamped transcript
- 0:00
[upbeat music] All right.
- 0:16
Thank you so much, Basil, for the introduction. Hello there, those of you in the audience. Thank you so much for joining us today. My name is Kevin. Uh, technically, we don't really have titles, so I am member of technical staff at Anthropic, working on the applied AI team.
- 0:31
Uh, before this, I joined Rippling to help build their FDE function. I was the first person to join that team, and we grew it to, uh, around twenty-five in a year.
- 0:40
Um, and so that's pretty cool. And then before that, did a bunch of stuff at Palantir. But, you know, list of companies is not really that interesting, right? Because we're talking about a function, and what I, I hope you're all here for is to hear about forward-deployed engineering.
- 0:56
Um, if you're here for, for evals or if you're here for, I don't know, videos of kittens, that's probably one room over. Uh, I'm also not qualified to talk about those things.
- 1:05
So I am trying to give a talk to you on Forward Deployed Engineering 101. So what I wanna do is walk you through the history of the role, the nature of the function, why Palantir chose to adopt FDE as its go-to-market motion, and then, right, extend that to maybe how you could apply it to your own organizations
- 1:28
and businesses. Um, and if possible at the end, would love to take any and all questions. All right. Does that sound good?
- 1:36
Yeah.
- 1:36
Yeah? Okay, that's too low energy. Does that sound good?
- 1:40
Yeah!
- 1:40
There we go. Oh my God, it's a conference, not a funeral. Let's go! Um, so, okay. High level, right? What does Palantir do? Palantir is a technology company that creates a technology platform, uh, a software platform called Foundry.
- 1:57
What Foundry does is Foundry enables organizations of arbitrary size to centralize all of their data in one place to create an ontology. What that means is to create proper nouns out of their data so that instead of having, you know, table one, table two, table three, if you have warehouses, you have a single table that's the source
- 2:18
of truth for warehouses. And then on top of that, Foundry enables companies to build applications. Okay. So if I was to explain that to some industry leader or other, they would be like, "Cool, you've made my data organized.
- 2:33
But what does that do for my actual business?" Right? A-and so that's, that's kind of where it falls short if you're just selling technology. Um, and then the other piece that's kind of interesting, right, is that you as a app-building platform, Foundry, your success is determined by how well your customers can use your particular piece of software.
- 2:52
And so there's also a huge tax, right? Not only are customers paying to invest in this platform, they are also needing to train up their people to get effective on it, and then, and only then, are they able to build things.
- 3:03
That is a terrible way to do business, and we soon realized that instead of selling just services or just products, you sell both. Um, so it's one combined thing where the customer is neither buying a piece of software nor are they buying the time of someone.
- 3:19
They are buying an outcome, right? You are sending over really smart people who will go and understand the nature of the customer's business, build them a solution on top of this platform, Foundry, and then the thing that you get in the end is that outcome.
- 3:35
Because if you are a, a leader of industry, right, if you're working in CPG, you care about, you know, getting more placement on the shelves, or you care about higher throughput of sales.
- 3:44
You don't really care how the data is organized, and nor should you care, right? That's more of an implementation detail. So wh-wh-where does this notion come from, this ridiculous idea of like sending engineers to the forefront?
- 3:55
Because I'm pretty sure of all the folks in the audience, you know, if you're familiar with software engineers, myself included, we're, we're some of the last people that should be customer-facing.
- 4:03
And so I wanna make this like really, really clear, okay? You can imagine this as a Punnett square. Um, it has to do with what it is you're selling and then who it is that's buying from you.
- 4:15
So if you sell a very technical platform or product, right? Let's forget Foundry for a second. If you sell a GitHub or if you sell, um, a Datadog, it is an incredibly complicated piece of software.
- 4:29
However, your ICP, right, are gonna be CTO, CIOs, and then your users are gonna be software engineers. They're gonna be people who can take and absorb this complexity and use it because it's part of their job.
- 4:40
The other situation is you are selling something that's not that complicated, um, and your buyer's not that technical, which is also fine, right? Say you have a tool, uh, something like a Rippling or something like a Jira or like a Slack, and those tools might be complicated, but they're configurable.
- 4:56
They're not meant to be developed upon. And so it's fine to be selling to a non-technical buyer.
- 5:03
You only need FDE if you are in this weird, unique situation of Palantir where you are having to sell something very technical to a non-technical buyer. Now, historically, why was that the case?
- 5:15
You know? Like, didn't Palantir like to do things easier than that? Well, it's because the nature of Foundry, right, which is an app-building platform, makes it inherently not as interesting to the large tech companies.
- 5:26
Uh, Google, Meta, what have you these days, the labs, they all have great software engineers, and they can build whatever apps the organization needs. Um, but when you're selling to say, uh, you know, Fortune five hundred client that works in oil and gas, they're not really gonna have that kind of engineering depth, right?
- 5:43
Their pipelines are not data pipelines. It's more gonna be, you know, uh, fluorocarbons or something like that. So for them to really get full value of your platform, you could either trust that they'll spend the time to, you know, not only buy your platform and use it, or you could just say, "Hey, here's the setup.
- 6:00
We will loan you some really good engineers that you don't have to hire, recruit, manage, or retain."
- 6:06
They will be trained in not only how to use this platform, but they will also, you know, work really closely with you in the same way that if you were at a fine dining restaurant, right?
- 6:15
The waiter is there to cater to your every need, and they will figure out how to solve you the problem and then build you the software. And so that ended up being the way that Palantir went to market with the Fortune 500, um, with the global Fortune 500, and, and how has that turned out, right?
- 6:31
'Cause, 'cause there's no point in me just getting on stage saying, "Oh, this is so cool," you know, "Here's the details," blah, blah, blah. So I'll give you some numbers.
- 6:39
If you look at the public SaaS companies in the Fortune 500, and you measure them by ACV, Average Contract Value. So, uh, you know, of any given customer, how much money is that customer spending with that particular vendor?
- 6:52
Palantir is first at four million, uh, last I checked. Next biggest is ServiceNow at one point two. Next biggest I wanna say is Workday at 600K, and then there is not a single public SaaS company that even cracks half a million ACV.
- 7:07
So just by these numbers, I would say it works pretty well. Um, Palantir is at some ridiculous valuation now, uh, a- and only at a few thousand headcount. Um, so okay, what is this FDE thing?
- 7:19
What is this model? What does this mean? Um, do we have any startups in the audience, or anybody working early stage? Yeah? Okay, some hands. So I'm sure you're familiar with the concept of a design partnership.
- 7:30
So in the early days of a startup when you don't know what your product is and your customers don't know what they're buying, you say, "Hey, let me work with you really closely.
- 7:37
Let me figure out what it is you need. I will, you know, spend my time, my energy, my technology, my resources. You just give me the context on what your problem is, and I'll build you a really good solution."
- 7:47
That's generally how most startups, at least in the B2B segment, find product-market fit. Um, FDE is basically taking this concept of a design partnership and scaling it up into enterprise, right?
- 8:01
That was the core assertion of Palantir was that who said, who said that design partnerships were only for the beginning stages of a company? Why can you not just do that at scale at enterprise?
- 8:13
Well, some of you in the audience who are very smart and observant might say, "Kevin, you can't do that [chuckles] in the enterprise because you can't maintain it. If you build something custom for every single customer, you are gonna be herding a whole bunch of cats, and you're gonna have, you know, a, a bunch of really shitty code
- 8:27
and, and no engineer is ever gonna work for me because they can't maintain it and no one wants to learn 55 repos." And you would be totally right. If you were to implement an FDE function where each FDE is building entirely from scratch, my friends, you do not have an FDE function, you have a dev shop.
- 8:42
Um, nothing wrong with that, of course. Those are really profitable businesses. But the thing that makes an FDE program different is that they are building on top of a platform.
- 8:52
They are never writing software from scratch, right? There is already a set of primitives on top of which they could assemble them into some application, some workflow, some solution that is arbitrarily valuable to their customers.
- 9:05
That is kind of the really key ingredient here because otherwise you are reinventing the wheel from scratch again and again, and before you know it, your P&L will eat you alive from the maintenance costs, um, if your engineers don't all quit first.
- 9:20
Okay, uh, I feel like I just said a lot of words. Do people have a general idea of what I'm talking about? Yeah? Some hands. Okay, great. Great. Oh my gosh.
- 9:29
Um, I'm, uh, I'm above where I thought I'd be. So okay, you're now saying, "Kevin, that's cool. You know, you've just told the story. You've given some frameworks. But then I, I'm not here to, to listen to you talk, right?
- 9:42
I wanna know how to apply this to my organization, to my business, how to bring this back to my team." Um, so how do you go about doing that?
- 9:50
First and foremost, and this is the thing that I advise to everyone who's thinking through the concept of Forward Deployed Engineering, is really ask yourselves, "Do I need an FDE function?"
- 10:01
Like do I need one? Not want, right? It's easy to want things that are in vogue. It's easy to want to do, you know, AI because that's what everyone else is doing.
- 10:09
But like do I need one? Do I have some corner case in my business where I must, must GTM a technically complicated thing to a non-technical buyer? If I don't have a situation like this, probably FDE is not the right fit.
- 10:25
There's a lot of great things you could do, uh, with DevRel and building a great developer engagement, uh, team if you're having a technical go-to-market motion. There's a lot of great things you can do with an SLG sales-led motion if you're doing more traditional SaaS, right?
- 10:38
It's only in this situation where you need FDE. That's the first piece. So the second piece is, do I have a platform? Or phrased another way, am I willing to invest in building one?
- 10:49
Because I assure you, right, no matter how tempting it is to, uh, have these engineers that can make you money, if they are not building on top of a platform with some number of shared primitives, you are in for a very bad time.
- 11:02
I, I just, I could not begin to stress the amount of maintenance burden that will be on your team even if you have a robust platform, um, never mind if you don't have one.
- 11:12
And so these are the questions that I would really encourage you to think about from an FDE 101 perspective of do I have to sell something complicated to a non-technical buyer, and do I have a platform on top of which my FDEs can build?
- 11:28
Okay, now for the, the AI piece because that's obviously happening in 2026. Um, what's changed since Palantir, uh, came onto the market, which I think was like 2004 or 2005, um, and now, is that, uh, artificial intelligence has made it really, really easy to build, really, really easy to write code, and also really easy to build sophisticated,
- 11:52
customizable software for customers. I mean, how many people in the audience are building agent for X? You know, insurance, legal, what have you, right? Um, I don't even need to see the hands for this one.
- 12:02
But the thing that's changed is not that the world has suddenly realized Palantir's FDE motion is a really good idea and they should do that. My personal hypothesis is that the thing which has changed is that the nature of doing business in the software industry itself is what's changed.
- 12:20
Because now nearly every platform is agentic, and that means nearly every platform is customizable, and that also means nearly all of you are gonna have a situation where your customers have no idea what the heck it is that you actually do.
- 12:35
And if you leave the success or failure of your product to their hands and to their ability to implement, I, I assure you this is not, uh, you know, like, gonna be an easy motion as you try and sell either into the upmarket or try and expand horizontally or vertically.
- 12:51
All right. I think that's enough words out of me. I would really like to hear some questions from the audience. Uh, anything and everything is on the table except my current work.
- 12:59
Thank you. [audience applauding] Uh, hands? Yeah.
- 13:10
You talk about needing shared primitives. Can you give a little more detail, like how atomic should these primitives be? Um, yeah, just some example of like-
- 13:20
Yeah. That's, that's a really good question. So... Oh-
- 13:22
Can you repeat the question first?
- 13:23
Oh, yes. So the question was, um, talk about shared primitives, how atomic should those shared primitives be? What does that mean? Um, so, okay.
- 13:33
If you are trying to sell a, uh, a, a platform where you're building, you know, something that involves data models, right? Um, perhaps one place to start is, is not having to, you know, define a data model from scratch.
- 13:47
Um, but I would say, you know, um, and this is a very lawyerly answer, is that it depends on what it is that you're getting into. Um, there's a lot of industries and a lot of situations where you can get away with having very robust primitives, right?
- 14:02
Where the app itself is, like, 60% built, and then people are just customizing the other 40%. Uh, and then there are certain use cases in certain industries and spaces where that's really not appropriate, and you, you need extremely granular, uh, configu- uh, configurations and, like, extremely granular tooling.
- 14:18
Um, a good example of a platform that I think all of you should be familiar with is AWS, right? Um, I'm sure, you know, many of the folks in the audience are really great engineers, and if you wanted to, you could, you know, buy your own server racks and then get them online and then maintain them.
- 14:33
But who's really done that since, you know, the 1990s? Um, but, like, within AWS, right, they give you a shared set of primitives, uh, like DynamoDB, so you don't have to, you know, invent a database from scratch.
- 14:43
Uh, but that's because they're trying to serve an extremely broad swath of customers. So it depends on your user base. Anyone else? Yes, right there.
- 14:52
No mic. Um, have you seen cases where two FDEs from two different companies collaborate? And if so, how, how's that been in terms of friction? In terms of what, what happens?
- 15:05
Yeah. So the question is on, uh, what is the collaboration mechanism, uh, you know, if two FDEs or multiple FDEs work on a project. I, I would say that's really encouraged.
- 15:15
Um, that's a really good pattern because, uh, especially when you're doing custom work for a customer, the last thing you want is, like, a single point of failure, right?
- 15:23
Where, uh, one person knows all the information, they go on vacation, and then you're kind of screwed. And so-
- 15:29
Two different companies.
- 15:31
Two different companies?
- 15:31
Yeah. You going, like, let's just say AWS sends an FDE to do a project. You're going in from Palantir.
- 15:37
Like work- working on the same project, like a bake-off?
- 15:40
Yeah.
- 15:40
Oh, okay. Or like a collaboration, like, like a partner?
- 15:44
Yeah.
- 15:44
Uh, y- yeah, yeah, that model exists as well. Um, it- it's just no different than having a contractor, right? Uh, you, you have to figure out who the contractor [chuckles] is in that situation.
- 15:52
Um, but that's kind of the mental model. All right, uh, right there in the back.
- 15:58
Hi. Um, what's the decision-making that goes on when, like... How do you determine when to change, like, whatever change that needs to happen is on, is on the platform side or, like, the forward deployed side?
- 16:10
That is a really good question. The question is, uh, what engineering changes go onto the platform versus what, uh, engineering changes go onto the forward deployed side. So anything that's bespoke and unique to a particular customer, um, is, uh, something that should really only exist for that one customer.
- 16:30
Anything that can be generalizable should be generalized in the long term. Now, when you begin, uh, your FDE-ing, right, probably you're not gonna have a lot of different primitives, but that's okay because FDE is also a great way to scout ahead and to find what additional product services you can build upon to further enable the success of
- 16:48
your business. Um, how we doing on time? Good? Oh, no, not good.
- 16:52
Last question.
- 16:52
All right, all right. Last question. Right there.
- 16:56
What is the perfect profile of an FDE?
- 16:59
Oh, this is so good. What is the perfect profile of an FDE? So the, the tagline that I will leave you with is that a FDE is nothing more than a customer-facing software engineer.
- 17:14
And so, um, it is a person who you would hire as a software engineer on your team, but at the same time, you would trust them in front of a customer in some shape or capacity.
- 17:25
And then the rest you'll have to figure out as you go because we are capped. Thank you so much. [audience applauding] [upbeat music]