`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.
Start a conversationWhat the team covers
AI and ML engineering: agents, voice, extraction, retrieval, evaluation
Back-end services in .NET, Java, Node.js and Python
Data engineering on Databricks and Spark
MLOps and DevOps: Kubernetes, Terraform, CI/CD, monitoring
QA and evaluation engineering
Solution architecture for regulated environments
What we own in production
Evals on every prompt or model change
Escalation rules and human-in-the-loop design
Audit logging and data residency
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.
Related work
All work →AI voice agents for patient calls
Production voice agents that take routine patient calls off clinical staff and write the next action back to the EMR.
Collections CRM and credit monitoring for a Tier-1 bank
Two production systems: a CRM for distressed borrowers and automated tracking of loan-covenant conditions.
Nine production subsystems for construction and urban-planning services
Permitting, scheduling, registries and inter-agency review workflows for a city government's construction departments.
START
One line is enough.
Tell us what you need to build or improve, and what the solution has to work with. We will reply with a practical next step.
GOOD TO KNOW
Frequently asked questions
What is an embedded engineering engagement?
One or more engineers work inside the client codebase, tooling and delivery rhythm on a defined technical outcome. Digital Gateway remains involved in delivery quality and handover.
Can you deliver a complete workstream instead of individual engineers?
Yes. Depending on scope, the engagement can include a lead, engineers, QA, architecture and project coordination for an end-to-end workstream.
Which skills can an engineering team include?
Relevant skills include applied AI, back-end systems, data engineering, cloud infrastructure, quality engineering and solution architecture. The mix follows the project.
Can the team work in our existing repositories?
Yes. Embedded work can use the client repository, issue tracker, communication tools and release process, subject to agreed access and security controls.
How do you define success for the engagement?
We agree the feature, integration, migration or operational result to deliver and review progress against working software, tests and production readiness.
Can you support AI and non-AI engineering together?
Yes. Applied AI often depends on back-end services, data pipelines, APIs and infrastructure. Those surrounding systems are part of the engineering work.
Do you work with our CTO and in-house engineers?
Yes. Delivery is coordinated with the technical owner and existing team, including architecture decisions, code review, release planning and handover.
What happens when we hire internally?
The scope and handover can change as internal capacity grows. Documentation, knowledge transfer and operational readiness are part of a responsible transition.
Can this work in a regulated environment?
The team can work within client requirements for access, data residency, logging and review. Our published work includes healthcare and financial systems.
How do we start?
Describe the work that is blocked, the stack and the outcome needed. We will discuss the right delivery shape and next steps rather than prescribe a fixed team size.