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AI pods
A dedicated AI team that owns your build, from discovery to production. An AI pod is a cross-functional team, assembled around a forward deployed engineer, that takes your AI initiative from first discovery to production and owns the outcome. Pangea.ai stands up your pod from the world's top 7 percent in days, not the months it takes to hire a team one seat at a time.
Builds and trains the models
Engineers the systems around them
Embeds in your environment
Owns the last mile into production
Builds the data pipelines
Owns deployment and monitoring
Owns testing and evals
Keeps quality high as the pod ships
What is an AI pod?
An AI pod is a small, dedicated, cross-functional team, typically three to six people, that takes a single AI initiative from discovery through to production and owns the outcome. You set the priorities; the pod handles execution, code review, quality, and delivery as one accountable unit.
That is what separates a pod from staff augmentation. With augmentation, you add individual specialists and manage them yourself. With a pod, you hand an outcome to a team that self-organises to deliver it. If you would rather direct individuals inside your own team, staff augmentation is the better fit.
Who is in an AI pod?
A Pangea.ai pod is built around a forward deployed engineer and sized to your initiative. A typical pod includes:
- A forward deployed engineer as the nucleus, the engineer who embeds in your environment and owns the last mile into production.
- AI / ML engineers, who build and train the models and systems.
- A data or MLOps engineer, who builds the pipelines, deployment, and monitoring the work needs to run in production.
- QA, to keep quality high as the pod ships.
- A product-oriented pod lead, who owns delivery and keeps the pod pointed at your outcome.
The exact mix flexes with the work. What stays constant is that the pod owns delivery as a team, with an FDE at its core.
How Talent Orchestration assembles your pod
Hiring a full AI team the traditional way takes months of recruiting, and you carry the risk if the mix is wrong. Pangea.ai assembles your pod instead. Using Talent Orchestration, we select a forward deployed engineer suited to your systems and outcome, then build the right team of specialists around them from the top 7 percent, and stand the pod up in days.
Because the pod is assembled for your specific initiative rather than pulled off a fixed bench, the mix fits the work: a retrieval-heavy product gets different specialists than an agentic workflow or a computer-vision build. The pod embeds, works to your priorities, and owns the path to production.
A pod, staff augmentation, or a fixed-price build?
The three delivery models differ by who owns the outcome and how the work is shaped. All three sit within Pangea.ai's AI development services.
Not sure which you need? Describe your project and our Talent Orchestration maps the options, then you choose.
Describe your projectWhat an AI pod costs
An AI pod replaces the months and overhead of hiring a whole team with a single, predictable engagement. In the wider market, managed AI pods are commonly quoted from around $12,000 a month, and weekly builder-pod retainers from around $4,500 a week, depending on pod size and seniority.
Hundreds of enterprises & SMBs trust Pangea.ai
Leading startups and enterprises worldwide trust Pangea.ai with their most important product initiatives.
Pangea.ai helped us with their very clear and structured agency selection process to find our perfect product partner — in just a few steps.
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Working with Pangea.ai was great. They made sense of a complicated and fragmented talent market — and we were able to find a great agency for our needs.
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The Pangea.ai team is professional and effective. Their offering of top talent from the very best agencies delivers.
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Stand up your AI pod in days.
A dedicated AI team, built around a forward deployed engineer, owning your build to production. Tell us the initiative and we will assemble the pod.
AI pod FAQ
Here are some of the most common questions we get, all ready for you.