Forward deployed engineering is having a moment. We've been doing it for years.

Forward deployed engineering is the term every AI company is reaching for right now. Palantir coined it. Scale built a business on it. Anthropic and OpenAI are pouring billions into putting engineers shoulder to shoulder with their customers.

The label is new. The work isn't, at least not for us. CloudFactory has spent the last three years doing exactly this: embedding engineers inside our customers' businesses to take AI out of the demo and into production. We didn't have a name for it. Now the industry does.

So while the term might be having its moment, this is familiar ground for us. Here's what forward deployed engineering actually looks like when you've been doing it for a while.

Why most enterprise AI never ships

Every company wants AI to change how their business runs. Most can't make it happen. They lack the in-house expertise to build it, and when they get it wrong, the consequences are expensive and public.

The model was never the hard part. Production is. There's a wide gap between an AI model that works in a demo and a system you can run your business on, and most organizations never cross it. Their proprietary data sits behind their firewall. The off-the-shelf platforms sit outside it. Nobody builds the bridge between the two.

That bridge is what a forward deployed engineer builds.

What a forward deployed engineer actually does

There are two kinds of “FDE” showing up in the market right now, and neither is what we do.

The first builds AI and stops there, heads down on the model, indifferent to how or where it actually runs. The second shows up to deploy their own software into your environment, whether or not it's the right fit. That's an app consultant with a better job title.

A CloudFactory forward deployed engineer does something different. They sit with you, learn your business, and use the best technology available to solve a real problem: our AI platform alongside whatever other tools fit best. They're not loyal to a stack. They're accountable for an outcome.

Prove it, then build it

The work happens in two moves.

First, we earn trust. An engineer embeds with your team, finds the pain that's costing you the most, and builds a rapid proof-of-value prototype to show we understand the problem and can solve it. Because of how we build, these prototypes come together in hours. That kind of work used to take weeks.

Then we make it real. Once a prototype proves the case, we integrate our platform into your production systems so you can run the solution day to day. For the right use case, that means going live in about four weeks.

How we deploy is up to you. Some customers want a hosted solution: they send us data, we process it, and we send it back. Others want everything to run inside their own environment, and we embed the platform there. The platform is built to work either way, so your requirements for security and control drive the decision, not ours.

Sometimes the smartest move is not to build

Here's the part that surprises people: a lot of the time, we'll tell you not to build at all.

If the thing you need is already available off the shelf, like invoice processing or contract management, buy it. We'll point you to a partner. Those are problems every company shares, and there's no advantage in custom-building what a customer can purchase today.

We build where it makes you different: on your proprietary data. One company we worked with had bought an AI tool at a conference, the same one any competitor could buy. Meanwhile they were sitting on 10 years of proprietary operational data that no one else had. That's where the real advantage was hiding. Telling you the difference is the whole job.

The proof

None of this works without the thing that's actually hard to build: knowing what works. We've worked across thousands of different AI use cases, which means we already know which approaches solve a given problem and which ones quietly fail in production. That's the expertise a forward deployed engineer brings into your business, and it's why the numbers below are possible at all.

The pattern is consistent: a prototype in hours or days, a working MVP in weeks, then iteration until the solution is delivering its full value in production. How long that last stretch takes depends on the problem. Some go fast. Others, especially when we're solving a big, structural problem rather than a narrow one, take a long, methodical approach on purpose, because getting it right matters more than getting it fast.

A few things we can put numbers on:

• Prototypes in hours or days, not weeks. The technology lets us stand up a working proof of value the same day, where it used to take weeks.
• A legacy modernization scoped at eighteen months, delivered in four. Not every engagement moves this fast, but when speed is what a use case calls for, our pattern reuse makes it possible.
• 5x faster processing. One damage-detection customer runs 100,000 image reports a month, each turned around in under two minutes versus the 15 to 20 minutes it used to take by hand.
• Up to 80% lower cost. For certain high-volume workloads, running on our platform instead of in-house cuts spend sharply. For some customers that's millions of dollars a year. But the bigger point isn't the cost line; it's that we go after the problems that are actually worth solving.

This isn't a side project for us. CloudFactory has been a trusted partner to more than 700 of the world's most ambitious AI companies since 2010, with over 100 customers running on the platform today.

Why we get faster every time

Every engagement makes the next one faster. We capture what works and what doesn't, turn recurring solutions into reusable parts of the platform, and go deeper in the industries we serve, so the second transportation problem we solve takes a fraction of the time the first one did.

To be clear about what that does and doesn't mean: we don't harvest your data or your intellectual property to improve our product. What compounds is our own expertise. Our team brings decades of experience in AI and across specific industries, and we bring the best of it to bear for you. Your data and your advantage stay yours.

Forward deployed engineering is part of the platform

For us, forward deployed engineering isn't a consulting bolt-on. It's the layer where customers experience and integrate everything else we build. It's a function we're growing, not a stopgap.

If you've got AI stuck in a pilot, that's the problem we exist to solve. Let's talk about getting started, prototyping, and moving to production.

FAQ

What is a forward deployed engineer?

A forward deployed engineer embeds inside a customer's business to solve a real problem with AI: first by building a rapid proof-of-value prototype, then by integrating a production system the customer can operate. At CloudFactory, an FDE uses the best available technology, our platform alongside whatever other tools fit best, rather than forcing a single stack.

How is CloudFactory's approach different from Palantir's?

Palantir pioneered the model, and we share the embedded, problem-first philosophy. The difference is that we're technology-agnostic and outcome-accountable. We'll recommend buying off-the-shelf software when that's the right answer, and we build custom only where a customer's proprietary data creates real advantage.

Is the platform hosted or deployed in our environment?

Either. Some customers send data to our hosted platform for processing. Others run everything inside their own environment, where our engineers embed the platform directly. Your security and control requirements decide which model fits.

How fast can we get to production?

Proof-of-value prototypes can be ready in hours. For the right use case, a production deployment can go live in about four weeks.

About CloudFactory

CloudFactory helps enterprises run AI reliably in production, especially in high-stakes use cases where errors create real business risk. We combine policy enforcement, expert validation and workflow control to ensure AI systems produce consistent, auditable outcomes. We then extend this foundation into production-grade agentic systems by integrating data, model and expert judgment into workflows that execute reliably across core business operations.

Trusted AI at Scale.