Embedded Engineering Pods
One to three senior engineers working inside your team structure, using your tools, your repos and your standups, rather than delivering from a separate vendor codebase.
Service
Forward-deployed AI engineers
Rather than handing you a proposal and disappearing until delivery, we embed one or more senior engineers directly into your team, with the same standups, same repo and same on-call rotation, to build the system and stay through the part that actually determines whether it survives contact with production.
What it is
Most AI vendor relationships are structured around a handoff: a team builds something in isolation, presents it, and leaves. That works reasonably well for software that does not touch live financial data or a regulated workflow. It works badly for AI systems, where the hard problems only show up once the system is inside your environment: a model that behaves differently on your real data than on the vendor's demo set, an integration that breaks against your actual API rate limits, a workflow assumption that does not match how your ops team actually works.
A forward-deployed engagement puts one to three senior engineers inside your team for the length of the build: in your Slack, in your sprint planning, with access to your staging environment from week one. They write code against your actual systems, not a sandboxed approximation of them, and they carry the pager for what they build through the stabilization period after launch, long after the demo.
This suits teams who have the roadmap and the domain knowledge but need engineering capacity that can move at the pace AI development actually requires: fast iteration against real data, comfortable working across the model layer and the surrounding application, and available to fix what breaks at 6pm on a Friday, beyond what shows up in the sprint review. It is closer to an extension of your engineering team than a vendor relationship.
What we build
Capabilities inside Forward-Deployed AI Engineers
02
Build → Deploy → Operate Ownership
The same engineers who build the system carry it through deployment and the stabilization period afterward, rather than handing off to a support team that did not write the code.
03
Full-Stack AI Capability
Comfortable across the model and retrieval layer, the application code around it, and the infrastructure it runs on, so an integration bug does not sit in a gap between two vendors.
04
Rapid Iteration Against Real Data
Working directly in your staging and, where appropriate, access-controlled production environments from early in the engagement, so problems surface in week two, not month four.
05
Institutional Knowledge Transfer
Structured handover of decisions, runbooks and architecture rationale as the engagement progresses, so your team can operate and extend the system without us.
06
Flexible Scaling
Engagement size flexes with the phase of work: one engineer for a focused build, a small pod for a larger integration, scaling down once the system stabilizes.
07
Incident Response During Stabilization
On-call coverage for what we built through the first weeks of real production traffic, when most of the genuinely hard bugs actually surface.
How we work
Delivery process
01Scoping & team fit
Define the build, the systems it touches, and the specific engineering skills the pod needs, then match engineers to that profile rather than whoever happens to be free.
02Embed
Engineers join your tools, repos and standups in week one, with the access needed to work against real systems, not a sandbox.
03Build in the open
Development happens visibly inside your existing sprint process, with your product and engineering leads able to review and redirect as it progresses.
04Deploy
Ship to production with the same team that built it, carrying the operational risk of the launch rather than handing it to whoever's on call that week.
05Stabilize
Own incidents and performance issues through the first weeks of real traffic, the period where most genuine production problems surface.
06Transition or extend
Hand over fully to your team with documentation and runbooks, or continue the engagement into the next phase of work: your call, made from actual delivery experience rather than a proposal.
What to expect
1‑3 engineers
typical pod size, sized to the build rather than a fixed package
Week 1
engineers have working access to your staging environment and are shipping their first pull request
Full ownership
through deploy and the stabilization period, beyond the build phase
Frequently asked questions
How is this different from staff augmentation?
Staff augmentation typically adds headcount to an existing plan. This is closer to bringing in people who have shipped several of these systems before and know where they tend to break: the value is as much the pattern-matching as the extra pair of hands.
Do your engineers need production access to our systems?
Access is scoped to what the build requires and follows your existing access-control policy: read-only where that's sufficient, time-boxed elevated access for deployment windows, always logged. We work inside your security review process, not around it.
What happens when the engagement ends?
We hand over documentation, runbooks and a walkthrough of the architecture decisions to whoever on your team is taking ownership. Several clients extend into a lighter advisory arrangement instead of a hard handoff; that is a choice, not a default.
Can engineers work inside our SOC 2 or ISO 27001 controlled environment?
Yes. Engagement terms, access provisioning and offboarding are built to fit inside whatever control framework you already run, and we'll go through your vendor security review directly with your team.
How quickly can a pod start?
Typically two to three weeks from scoping to an engineer with working access, assuming your side has the staging environment and stakeholder availability ready. Faster starts are possible for a narrowly scoped build.
Explore more
Related
Pair this with
Industries we apply this in
Talk to us about Forward-Deployed AI Engineers
A 30-minute call to scope what a first version would look like against your own data and systems.
Book a 30-min intro call