A senior AI engineering team, led by an architect who owns the outcome.
An AI Pod is a managed engineering squad for AI development. You meet your 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 that enterprise AI depends on: the models, data pipelines, and infrastructure beneath the application layer. It is the work we concentrate on, and where our experience is deepest.
Each pod is assembled around that focus and supported by an architect who has delivered production AI for enterprise and scaling teams before.
Model training & fine-tuning
Adapting and optimizing models for your domain, not just calling an API.
Data & inference pipelines
The data engineering and serving infrastructure production AI runs on.
Agent systems & orchestration
Multi-agent architectures, tool integration, and autonomous planning loops.
Evaluation & governance
Measurement, guardrails, and oversight so AI behaves predictably in production.
The model
Every pod is two accountable layers.
You do not hire engineers from a list and hope they gel. A pod is structured so someone always owns the result.
Engineering Manager
The architect and your primary partner.
You meet the Engineering Manager first. They scope the work, set the technical direction, assemble the squad, and own the delivery outcome from start to finish, including day-to-day delivery and code quality.
AI Engineers
The delivery squad, curated for you.
Senior, AI-capable engineers, curated into your portal for you to review and interview. They join your standups, repositories, and channels as an extension of your team.
How it works
From signup to a started pod.
Self-serve to start. There is no sales gate and no manual scheduling, but matching and team assembly stay human-led.
Sign up, describe the work
Create an account and outline your project, stack, and goals through a guided intake. No sales call to book.
Meet your Engineering Manager
Within 48 to 72 hours you are matched to an Engineering Manager suited to your domain, and you schedule a conversation directly.
Your EM assembles the pod
After scoping, your Engineering Manager curates a shortlist of engineers into your portal.
Review and interview
You review curated profiles and self-schedule interviews. Approve or decline each candidate on the pipeline.
The pod starts
Published pricing, month-to-month terms, automated billing, and an immediate start. The platform runs the engagement from here.
The platform
One platform for the whole engagement.
Once a pod is running, the platform becomes your day-to-day system for the team: roster, interviews, performance, 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
Terms that remove the risk of starting.
Pricing is published and there is no hidden markup. Engineers are legally employed in Vietnam, and you receive a single 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 & NDA
Clean contracts, surfaced in the platform.
Weekly reporting
Delivery updates on a fixed cadence.
Talent passes a six-stage, human-run 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.
Tell us about the AI work ahead. We will match you to an Engineering Manager suited to your domain and stack, with no sales gate in the way.