Technical docs

AI teammates that plug into the way your team already works

The full loop: connectors, backlog, plan, build, controls, and return. For the short operating 101 — AWS, IAM, assign, chat, deploy — start at How it works.

The loop, in one view

Work enters from your tools, runs through VPods, and lands back in your GitHub and AWS.

Flow: connect your stack, give work on the backlog or in Teams, VPods plans and builds under scoped controls, then code and deploys return to your repositories and cloud. Select a step to read it.

Your stack
VPodsWorkforce layer
Your stack

1 of 6 · Connect

Connect your existing stack

Start with what you already have

VPods plugs into the systems your engineering organization already runs.

  • GitHub repositories
  • AWS account
  • Microsoft Teams
  • Jira
  • Team roster and available AI teammates

One-time setup. Your existing systems stay the systems of record.

studio · connections

  • GitHubvpods/aether · writeConnected
  • AWSrole/vpods-deployConnected
  • Microsoft TeamsAether stand-upConnected
See the full architecture

Connectors, entry surfaces, the required reasoning loop, and execution in your AWS account via a scoped IAM role.

VPods architecture

Merge into the stack you already run

Connectors attach to GitHub, AWS, Teams, roster, and Jira. Work enters through backlog, Kanban, or Teams — same grammar. Reasoning stays inside VPods. Execution lands in your AWS account through a scoped IAM role. Nothing sits above that account as another platform to integrate.

Flow: connect GitHub, cloud, Teams, roster, and Jira once. Work enters from story backlog, Kanban chat, or Microsoft Teams. Conversation agent, context manager, then task orchestrator. Planning, coding, and review all pass through a required ReAct loop, then the tool manager, then commits and Bedrock inside the customer AWS account.

Step 0 — connect once

Before any backlog or chat works

GitHub

Pick repo

Setup

Cloud connect

One-time setup

Setup

MS Teams app

Install once

Setup

Save roster

Pick agents, capacity

Setup

Jira

View + comment, IDE only

Setup
Setup

Entry surfaces

Same grammar, whichever door you use

Story backlog

New story or import

Entry

Kanban chat

Chat a card

Entry

MS Teams

Assign + status

Entry
Entry

Conversation agent

Intent, scope, project

Reasoning

Context & memory manager

DynamoDB + packed context

Entry

Task orchestrator

Workflow, retries — Kanban

Reasoning

Planning

Architecture

Entry

Coding

Files — live in IDE

Core

Review

Validate

Entry

ReAct agent loop

Reason → act → observe → repeat

Required

Tool manager

Blocks direct cloud / API calls

Boundary
Customer-owned

Customer's own AWS account

Reached via scoped cross-account IAM role — not VPods infrastructure.

via IAM · VPodsDeploy

GitHub

Commits to your repo

Your AWS

AWS Bedrock

Compute + your models

Your AWS
  • One-time setup
  • Reasoning
  • Required loop
  • Customer-owned, via IAM

What changes for your organization?

Your people stay on the decisions that need judgment. VPods takes more of the execution.

Before VPods

  1. Understand
  2. Plan
  3. Code
  4. Test
  5. Review
  6. Deploy

With VPods

  1. Assign
  2. Collaborate
  3. Review
  4. Ship

Start small. Expand when it works.

Start with one project. Connect the systems. Give an AI teammate a real piece of work. Expand when the team has confidence — the same way you would add people.

Adding a hire

  1. Week 1–3

    Write the req. Post. Wait.

  2. Week 4–8

    Screen. Interview. Loop.

  3. Week 9–12

    Offer. Notice. Onboard.

Valid path · ~12 weeks of wait

vpods.ca/pricing

Active

Adding a pod

Solo · live today

  • Checkout complete
  • Pod on your board
  • Works alongside your engineers

Common questions

Do we have to change our tools?

No. GitHub stays your source of truth. AWS stays your execution environment. Jira and Teams stay how work reaches you. VPods connects to what you already run — it doesn't ask you to migrate anything or adopt a second system of record.

Does the AI get unrestricted access?

No. Every action that reaches your cloud or connected systems passes through a controlled tool layer — agents never call AWS, GitHub, or Jira directly. Cloud access is a scoped cross-account IAM role limited to what's needed for delivery, with a deny-list on high-risk or expensive actions regardless of plan tier.

Who owns the code?

You do. Agents commit into your own GitHub repository. There's no separate VPods code store and no export problem — the repo you already have is the repo they work in.

Is there a free trial?

No — there's a paid Trial plan at $25/month (1 agent, capped tasks, hard stop at the limit), not a complimentary period. AI inference has a real cost from the first token, so every plan is a paid subscription from day one.

How do we start?

Connect GitHub, connect your cloud account, install the Teams app, and save a roster — a one-time setup. Once that's done, put a story on the backlog or just chat an agent by name, and it starts working.