Engineers pair-programming at a shared workstation in a modern office

OUR MODEL

The engineers come to you.

Forward Deployment Engineering is the difference between a vendor who delivers a document and a partner who delivers a working system. We embed senior AI/ML engineers inside your team and stay until it's live.

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Reality breaks specs. Embedded teams absorb it.

AI moved from demos to operational deployment. The bottleneck now is getting agents working inside messy, regulated, legacy environments — claims systems, EHRs, core banking. Embedded engineering is the category-correct answer.

Strategy consultancies deliver decks. Dev shops deliver code against frozen specs. Both break on contact with reality. An FDE pod absorbs the messiness in real time — we sit in your standups, work in your repos, and ship.

The result reaches production faster because there's no translation layer between "what was specified" and "what was needed."

Context matters more than code

Consultants leave. Contractors ship to spec. FDEs embed long enough to understand the real constraints: the legacy system everyone forgot about, the data quality trap, the security policy that kills half the shortcuts.

Senior by default

Every pod member is senior-level, fluent in model selection, agentic architecture, and production observability. You're not managing juniors or debugging vendor mysteries.

Production is the only deliverable

FDEs don't hand off a working notebook and vanish. The deliverable is a system that carries real workload, with real users, instrumented and documented for your team to own.

You own everything at the end

All code, infrastructure, documentation, and observability dashboards live in your environment. No vendor lock-in, no proprietary layers. When the pod cycles to your next use case, you run it.

What an FDE pod looks like.

A small, senior team deployed against one outcome:

LEAD FDE

Lead Forward Deployment Engineer

Owns the architecture and the relationship.

AI/ML ENGINEERS

Applied AI/ML Engineers

Model selection, agentic systems, MCP, evals.

PRODUCT ENGINEER

Product Engineer

The application, the interface, the integration surface.

No layers, no juniors learning on your dime, no handoffs that lose context.

Industries where embedded engineering unlocks velocity.

Insurance

Unstick claims intake, underwriting, and submission processing.

Healthcare RCM

Prior-auth, denials, revenue cycle workflows that regulatory deadlines are forcing.

Fintech

Back-office reconciliation, compliance/AML ops, reporting automation.

AI Platforms

We're the deployment capacity that gets your agents live inside your customers.

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Everything. The code, the infrastructure, the knowledge.

We deploy on your cloud and hand over a system your own team can run and extend. FDE is about transferring capability, not creating dependency.

Stop advising. Start shipping.

Your next AI system is 6 weeks from production. One senior pod, embedded in your team, owning the outcome from day one.

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