`EMBEDDED TEAMS`

Your roadmap does not wait for hiring.

Senior engineers who join your team for the AI work your open roles were meant to cover. They work in your repo, your tools and your sprint rhythm, and hand over cleanly when your hires arrive.

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What the team covers

01

AI and ML engineering: agents, voice, extraction, retrieval, evaluation

02

Back-end services in .NET, Java, Node.js and Python

03

Data engineering on Databricks and Spark

04

MLOps and DevOps: Kubernetes, Terraform, CI/CD, monitoring

05

QA and evaluation engineering

06

Solution architecture for regulated environments

What we own in production

01

Evals on every prompt or model change

02

Escalation rules and human-in-the-loop design

03

Audit logging and data residency

04

Staged rollout and monitoring

Typical team shape

One engineer — fills a specific open role, works inside an existing squad.

A pod (2–4) — owns one AI feature end to end, with its own lead.

A delivery team (5+) — PM, architect, engineers and QA for a full workstream.

QUESTIONS

Frequently asked questions

How fast can someone start?

Usually within about two weeks of agreeing on the team. [CONFIRM]

Do they work our hours?

Yes. We are Vancouver-led and work US hours, so the overlap is a working day, not a handoff.

Fixed price or time and materials?

Time and materials, billed monthly. Scope changes as you learn, and fixed price would fight that.

Who manages the engineers?

They work inside your process and report into your engineering leads. A Digital Gateway lead stays responsible for the team's quality and staffing.

What happens when our hires arrive?

We hand over code, context and documentation, and step back. That is a planned outcome, not a failure.

Can you sign our security and compliance terms?

Yes. We work under NDA, least-privilege access and your audit requirements, including HIPAA and SOC 2 controls.

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