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Organizations have more technology than ever, and still struggle to turn it into outcomes. Their businesses span multiple clouds, dozens of systems of record, siloed SaaS products, and legacy applications. Meanwhile, better AI models and tools arrive every week. Someone has to decide what fits, connect it securely, and keep it working as everything changes. That work consumes technical teams and leaves too many good ideas waiting.

Atlas takes that work on. Describe the outcome you need, and Atlas brings together the right capabilities, builds the system, and keeps it running and current in your cloud. Complexity stays out of the way. Every layer stays open to inspection, and your enterprise stays in control.

Our goal is to put more of the business to work on what matters. The people who understand your customers, run your processes, and see opportunities firsthand should be able to act on what they know.

Innovation should be an everyday habit of the business: see a problem, describe the outcome, put a solution into operation, keep improving. Every team, every day.

Industry-specific vendors sell you their idea of your industry, and you adapt to it. That was the SaaS deal: your business bends to the software.

AI flips it. For the first time, you can have software built around how your organization actually works, and it can be the best in your category because it's yours. Your workflows, your data, your edge, not a template your competitors also bought.

Atlas makes that practical. It builds AI systems around your exact workflows on one platform. Ontology, a living map of your data, systems, and processes, gives every agent the same understanding, so what one learns, the others know.

Stacking vertical solutions pulls you back into the old model. Each sees a slice of your business, and someone has to connect them and keep them in sync. That's the SaaS silo problem all over again.

With Atlas, every new AI system joins a connected whole. Reuse agents, share learning across workflows, and stay current as the business changes. Build and Release put those systems into production and keep them running in your cloud, with the same security and control everywhere.

Atlas enables a new way of working. Business teams, IT, and developers can focus on an outcome while Atlas handles the coding, data connections, testing, security, and DevOps needed to deliver it. That continues into operations, including cost optimization, security patching, and scaling.

Your systems run in your cloud. You can look into each layer of the stack, inspect what Atlas has built, and verify how it works. Your identity, permissions, and policies govern what people and AI can access and do.

That is the purpose of transparent abstraction: you can inspect the details whenever you need to, without having to manage every detail yourself. More people can participate in innovation and deliver outcomes faster, while the enterprise retains its sovereignty, security, and control.

For teams that find this a big change, a practical starting point is a business-critical project in the backlog or a new business process or workflow. Teams can build confidence by delivering a concrete outcome, then expand from there.

Yes. For time-sensitive outcomes, Atlas FDEs work alongside your team to build the semantic layer in Ontology and deliver the systems you need.

Using Atlas, they accelerate delivery while training your team to use the platform, understand what has been built, and continue improving it as your needs change.

The goal is lasting ownership. Your team should be able to build independently and take control of what comes next. Sovereignty includes having the confidence, knowledge, and ability to run and improve your systems without depending on FDEs for every change.

Keep using them. Atlas works with coding agents and uses several of them to build. But code is only one piece of a system that has to run in production.

Take a hospital processing thousands of referrals a day. Agents have to securely pull patient records, confirm that required tests and eligibility information are in, prepare documentation for clinicians, and flag urgent cases for review. The goal is a timely appointment, with the patient prepared and the clinician informed.

A coding agent can write the logic for that. Running it reliably takes more: shared context across agents, connections to health systems, testing, infrastructure that scales, and controls that enforce business rules, permissions, and security policies every time, whatever the model does. Different agents may also need different models for their tasks.

Atlas provides those pieces and keeps them working as requirements and technology change. Your teams keep using coding agents to explore ideas and write code. Atlas turns that work into a secure, maintained system in your cloud, built around the outcome you need.

Atlas is what Palantir would build if it started today: AI-native from day one and designed to speed up business outcomes. It covers much of the same ground as Foundry, AIP, Apollo, and Ontology. The difference is who gets to build with it.

With Atlas, business teams build production-grade systems, with IT and developers involved as needed. They describe the outcome, and Atlas handles the infrastructure, development, testing, and operations.

Depending on your team's readiness, you may not need forward deployed engineers at all. If you do bring them in, their job is to make your team self-sufficient.