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Automated by Agents.Supervised byAI-Native Experts.

Compact teams of AI-native engineers armed with agentic workflows and reusable components to deliver more output, more reliably, with less delivery overhead.

Build Your Pod

TRANSFORMING LEADING ENTERPRISES FOR THE AI ERA

AES
Bayer
Coca-Cola
Danone
Gilbarco Veeder-Root
Goodyear
Jose Cuervo
Mastercard
McCain
Mercedes-Benz
Oracle
Pan American Energy
Roche
Sanofi
SAP
Toyota

What Is an AI Pod?

An AI Pod combines a lean core of senior AI-native engineers with specialized agents and reusable workflows. Each Pod is configured around a defined initiative, workflow, or backlog. Agents automate execution-heavy tasks while engineers set direction, manage enterprise context, supervise results, and remain responsible for quality.

Your Need

A defined initiative or workflow

INCLUSION CLOUD AI POD

Senior Engineers Supervise
AI Pod Lead setting direction
AI Pod Lead Set Direction
Configure & Execute
AI Engineer supervising outputs AI Engineer
Domain Specialist refining outputs Domain Specialist
Supervise & Refine
AI Agents Execute
Production Outcome

Tested and ready to use

Reuse & Scale

More Automation.Less Overhead.Engineers in Control.

Why are AI Pods better suited for AI delivery?

AI Changes the Nature of Work.
Delivery Models Should Adapt Too.

AI agents can take on more of the execution-heavy work across delivery, from research and requirements to code, testing, documentation, analysis, data processing, and workflow actions.

Many companies are trying to layer that capability onto traditional headcount-based delivery models. AI Pods are structured around a different model: a leaner team, more agent-assisted execution, and senior engineers who remain accountable for quality.

Leaner than traditional delivery.More controlled than vibe coding.

Not a Tool. Not a Hire. A New Delivery Model.

Engagement modelStaff Augmentation
Engagement modelDedicated Team
AI-native deliveryAI Pod
What you add
Individual resources
A traditional delivery team
An AI-native delivery capability
Built around
Roles and hours
Team composition
A defined outcome
How work gets done
Primarily human execution
Primarily human execution
Engineers direct, agents accelerate execution
How capacity grows
Add more people
Expand the team
Improve agents, workflows, and reusable assets
Who manages delivery
Your internal team
Shared or provider-led
Inclusion Cloud’s senior engineers
Who owns quality
Your internal team
Distributed across the team
Senior engineers supervising every critical result

Enterprise AI Takes More Than Vibe Coding.
It Takes a Structured Delivery System.

Compare delivery approaches 01 / 07
Vibe Coding

Prompt-by-prompt execution

Inclusion Cloud AI Pod

Repeatable agent-enabled workflows

How it works

From Defined Work to Engineer-Validated Results.

01

Define

Clarify the workflow, scope, systems, constraints, business goals, and success criteria.

02

Configure

Set up the Pod: specialists, agents, tools, enterprise context, integrations, and access.

03

Execute

Agents and AI-enabled workflows perform repeatable, execution-heavy work while engineers stay in the loop.

04

Supervise

Senior practitioners review output, resolve exceptions, enforce standards, and remain responsible for quality.

05

Reuse

Capture validated patterns, prompts, connectors, tests, playbooks, and knowledge for future work.

When to use an AI Pod

Launch AI Initiatives Without Disrupting the Core.

You want to implement AI without creating a dedicated internal team

Gain the engineering, data, and AI capabilities required to move an initiative forward without hiring and assembling an entirely new function.

You want delivery, not more people to manage

Avoid adding disconnected resources through a body shop or coordinating multiple agencies. One compact Pod brings together the engineers, agents, workflows, and delivery accountability.

You need to turn an AI concept into a real product

Move beyond a prototype with the engineering, integration, testing, monitoring, and enterprise controls required for production.

You need to move fast without the risks of vibe coding

Vibe coding can accelerate a prototype, but its outputs can be inconsistent, difficult to audit, and unsafe to deploy without proper review. An AI Pod adds senior engineering supervision, testing, security controls, and clear accountability from the start.

You need innovation without slowing down core initiatives

Run AI initiatives in parallel with core programs so your internal team can keep its focus while the Pod moves new work forward.

Find your AI Pod
Can’t Find the Pod You Need?
Tell us what you want to build. We’ll match you with the right Pod for your needs.