A small team of three to five engineers that owns a product area end to end, with AI engineering built in and one lead accountable to your team.
Why pods exist
A single embedded engineer is right for a focused problem. A product area, a platform rebuild or a new AI feature line often needs a few complementary skills working as one team, without hiring each role yourself.
A pod gives you that team with one lead, one agreed outcome and the same ownership standards as a forward-deployed engineer.
What you get
Each pod is shaped around the work. These are the commitments behind every pod; exact roles and scope are agreed with your team.
Three to five engineers work as one unit on a defined product area or initiative: a new AI feature line, a platform rebuild, an internal tool suite. The pod owns delivery, not a backlog of disconnected tickets.
What to verify: Check that the pod has one agreed outcome, a written scope and a named lead accountable to your team.
Each pod is shaped around the work. A typical pod pairs an AI or LLM engineer with full-stack, backend or data engineers, and adds QA or DevOps where the release path needs it.
What to verify: Ask which roles are in the pod and why each one is needed for the agreed outcome.
One senior engineer leads the pod, joins your planning and reviews, and reports progress against the sprint goal in plain language.
What to verify: Look for a regular written update covering what shipped, what is blocked and which decisions need you.
Architecture notes, runbooks and evaluation tooling are written as the pod works, so your team can run and extend what was built when the pod changes shape or steps back.
What to verify: Verify handover artefacts and a named owner on your side before the engagement ends.
AI in the engagement
AI is not a separate team. Engineers who ship AI in production sit inside every pod.
Every pod includes engineers who ship AI in production: agents, retrieval, evaluation and monitoring are built alongside the rest of the product, not handed to a separate team.
AI features ship with test sets, review workflows and audit records, so quality is measured rather than assumed.
Evaluation harnesses, prompts, dashboards and pipelines built by the pod stay in your repositories and your cloud.
Grow with evidence
Most pods begin with one engineer or a two-week sprint. People are added only when the work justifies it and you agree.
Working principles
Scaling rules, stable people and visible trade-offs decide whether a pod actually works. We make those commitments explicit rather than implied.
Most pods begin with one engineer or a two-week sprint. People are added only when the work justifies it and you agree.
The pod adapts to your planning, review and release rituals rather than running a parallel methodology.
We aim to keep the same engineers on the pod for its lifetime. Any change is discussed with you first, with a handover period.
Significant decisions are written down with their alternatives, so your team can see why the product is built the way it is.
Security & trust
A pod touches your code, your data and your customers. Access, ownership and data handling are agreed in writing before the first commit.
Repository, cloud and data access is scoped per engineer and agreed before work starts. Access is removed when the engagement ends.
Code, documentation and models built by the pod belong to you, under terms agreed in the contract.
Pods work inside your environments and follow your data handling rules. Sensitive data stays where you decide it lives.
Working arrangements
The pod works remotely inside your rituals, in your cloud and your repositories. On-site visits are arranged where being in the room genuinely helps.
Pods work remotely inside your tools and rituals, with overlapping hours agreed up front.
Work happens in your repositories, your cloud accounts and your ticketing system, so nothing lives in a parallel world.
Visits for kickoffs, workshops or launches are arranged where being in the room genuinely helps.
How it starts
A paid two-week sprint proves the working relationship before anything longer is discussed. If it does not show real progress, you walk away.
We agree the product area, the outcome the pod owns and how progress will be judged.
We propose the roles needed, from three to five engineers, and explain why each one is there.
A paid two-week sprint with one agreed goal shows how the team works before anything longer is committed.
The pod ships in regular increments. Size and mix are reviewed with you as the work changes.
A forward-deployed engineer is one senior person embedded in your team. A pod is a small team of three to five that owns a larger product area or initiative, with its own lead.
Yes. Many pods start with one engineer or a two-week sprint and grow only when the work justifies it.
The pod lead runs day-to-day delivery and works with your product or engineering lead on priorities. You set direction; the pod owns execution.
Yes. Size and mix are reviewed with you regularly and adjusted when the work changes, with notice agreed in the contract.
No. Pods build complete products: frontend, backend, data, infrastructure and QA. AI is part of the work where it earns its place.
You do, under the terms agreed in the contract. Work happens in your repositories from day one.
Pricing depends on the size and mix of the pod and is agreed before work starts. The first step is a paid two-week sprint.
We hand over documentation, runbooks and tooling, and agree a transition plan so your team can run what was built.
Let's talk
Tell us about the product, the users and the constraints. We will help define a first sprint goal and the evidence needed to judge it.