AI Pods
A senior AI engineering team, led by an Engineering Manager who owns the outcome.
An AI Pod is a managed engineering team for AI work. You meet the Engineering Manager first. They scope the work, assemble the team with you, and run the engagement through one platform.
Why AI Pods
Focused on enterprise AI engineering.
AI Pods are staffed for the engineering beneath the application layer: models, data pipelines, and infrastructure. That is where our experience is deepest. Each pod is led by an Engineering Manager who has delivered production AI before.
Model training and fine-tuning
Adapting and evaluating models on your domain data.
Data and inference pipelines
The data engineering and serving infrastructure production AI runs on.
Agent systems and orchestration
Multi-agent architectures, tool integration, and autonomous planning loops.
Evaluation and governance
Measurement, guardrails, and review gates for systems in production.
The model
Every pod is two accountable layers.
A pod is structured so that one person owns the result.
Engineering Manager
Your primary contact and the accountable lead.
You meet the Engineering Manager first. They scope the work, set the technical direction, assemble the pod, and own delivery and code quality for the whole engagement.
AI Engineers
The delivery team, shortlisted for you.
Senior engineers with production AI experience, shortlisted into your portal for review and interview. They join your standups, repositories, and channels.
How it works
From signup to a started pod.
Self-serve to start. Matching and team assembly are done by people.
Sign up, describe the work
Create an account and describe the project, stack, and goals in a guided intake. No sales call required.
Meet your Engineering Manager
Within 48 to 72 hours you are matched to an Engineering Manager with relevant domain experience. You schedule the first conversation directly.
Your EM assembles the pod
After scoping, the Engineering Manager shortlists engineers into your portal.
Review and interview
Review the profiles, schedule interviews, and approve or decline each candidate.
The pod starts
Published pricing, month-to-month terms, and automated billing. The pod starts once the roster is approved.
The platform
One platform for the whole engagement.
Once a pod is running, the platform holds the roster, interviews, reporting, compliance, working time, and invoices.
One overview of the whole engagement
Team size, hours committed, interviews in progress, and pipeline status in a single view.
Commercial terms
Published pricing, monthly terms.
Pricing is published and there is no markup on top of it. Engineers are employed in Vietnam, and you receive one invoice in USD.
Month to month
30-day notice to scale up or down.
14-day replacement
A replacement guarantee on every engineer.
IP assignment and NDA
Signed once, visible in the platform.
Weekly reporting
Delivery updates on a fixed cadence.
Every engineer passes a six-stage interview pipeline before joining a pod: screening, deeper technical, system design, architecture and DevOps, client team, and client CTO. Delivery quality is owned by the Engineering Manager, day to day and at the engagement level.
Start an AI Pod
Meet an Engineering Manager and scope your first pod.
Describe the work. We match you to an Engineering Manager with relevant domain and stack experience.
- Describe the work through the intake
- Matched to an Engineering Manager
- Scope agreed, shortlist in your portal
- Interviews, approvals, and a start date