Key Takeaways

  • Sequence Holdings partnered with the Dell Family Office on a $7.7 billion take-private transaction of insurance brokerage Baldwin to overhaul its operations with proprietary AI systems.
  • Engineers coming from Palantir and Scale AI noticed that 80% of enterprise software infrastructure is homogeneous across verticals, while only 20% is industry-specific.
  • Sequence proved out this playbook at regional lender BankSouth before applying the exact same core infrastructure to insurance.
  • The operational model bypasses third-party enterprise SaaS vendors entirely by establishing a permanent holding company that owns both the software and the equity.
  • Technical teams can standardize enterprise automation using Sequence Holdings' 4-Layer Atlas Platform Architecture.

The Sequence Holdings' 4-Layer Atlas Platform Architecture

When Sequence Holdings acquires an incumbent, engineers do not build bespoke point solutions from scratch. They deploy Atlas, a shared technical substrate designed to make enterprise operations legible to machine learning systems.

  • Layer 1: Data Ontology: Defines the organization and core business motion in code, making the business legible to AI models by interrelating customer entities, policies, claims, and loan systems.
  • Layer 2: Agent Builder: Builds high-performance AI agents strictly grounded in the organization's verified ground-truth data.
  • Layer 3: Lattice (Orchestration Engine): Instruments multi-agent workflows across the enterprise to execute coordinated operational tasks.
  • Layer 4: Artifacts (Application Builder): A top-level application-building layer that enables engineers and operating company teams to build custom end-user interfaces and applications on top of the underlying system.

As Lee explained, this architecture stems directly from prior engineering patterns: “If you break down the business to its atomic units, 80% of it is largely homogeneous and 20% of it is vertically specific.”

By separating the core platform from vertical logic, infrastructure built for banking translates directly into insurance. “The first layer is a data ontology,” Lee noted. “Think about that as just simply how do we define the organization, the motion of that business in code, and that's extremely important. The way that we talk about it internally is: how do we make the business legible to models?”

Once that base exists, higher layers take over. “The layer on top of that is our agent builder, which is basically how do we build high performance agents grounded in ground truth,” Lee said. “The third component to it is what we call Lattice, which is our orchestration engine to instrument workflows. On top of that is Artifacts, our application builder that sits on top of everything we have built. Foundationally, all the core infrastructure we have built is reusable at Baldwin and any future company that we do.”

When This Works (and When It Doesn't)

This framework works when you deploy AI across operations that handle structured, high-volume transactions like insurance underwriting, credit approvals, and claims management. In these businesses, the underlying mechanics consist of rules, database records, and repetitive manual validations that can be mapped into explicit code.

It fails if your company operates with messy, subjective judgment calls where no ground-truth records exist. If two human operators cannot agree on the right outcome for a given record, Layer 1 will produce corrupted data, Layer 2 agents will hallucinate, and Layer 3 workflows will automate bad decisions at scale.

What to Do With This

Audit your internal product stack this week. Map every backend workflow your engineers maintain into the four Atlas layers to find where your custom code is wasted.

Start with Layer 1: write an explicit schema that defines your company's core entity relationships in plain code rather than letting them hide inside scattered Postgres tables and third-party dashboards. If an LLM cannot inspect a single ontology to see who your customers are, what they bought, and what status their account holds, stop building customer-facing AI agents until you fix that base layer.