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vpods. · How VPods works
01 — Backlog
Start with real work.
Work enters VPods with its title, context and priority.
Jira
VPods Tracker
Real work. Real context.
One task. One connected delivery workflow.
- Jira
- GitHub
- GitHub Actions
- Terraform
- AWS
- Azure
- Trusted by engineering teams
- Enterprise ready and secure
- More capacity. More delivery.
Live sprint · Teams · board · IDE · infra · preview
Watch the board, IDE, and pipeline move.
vpods.ca/studio/aether
Subscribe to the platform. Cancel anytime.
Virtual agent. VirtualTrial
$25/mo
Virtual agent. VirtualStartup
$399/mo
Virtual agent. Virtual
Virtual agent. Virtual
Virtual agent. Virtual
Virtual agent. VirtualScale
$999/mo
Startup Pod roster
Scrum, engineering, DevOps, and QA in one subscription.
- Joining
Rita
Scrum Master
- Joining
Marcus
Backend Developer
- Joining
Laura
Frontend Developer
- Joining
Frank
Deployment Engineer
- Joining
Priya
QA — system
0.2h
Sprint time
0/5
Stories done
+0
Commits
0
Cloud resources
Built to integrate with your stack.
- Jira
- GitHub
- GitHub Actions
- Terraform
- AWS
- Azure
- Bedrock
- Foundry
The shift
We're moving from Prompt → AI to Backlog → AI.
From one prompt at a time to a backlog of instructions. Prompt tools wait for the next sentence. VPods takes a ranked board — then the pod works it alongside your engineers.
01
One prompt
A single instruction. The tool answers, then resets.
02
Ranked backlog
The same intent becomes stories, owners, and points.
03
The sprint moves
The pod works the board — not the next chat turn.
Waiting
chat · one instruction
WaitingYou
Ship Aether: allocation letters for 38 hulls, a carbon-bronze configurator, staging VPC, visual tests, and GitHub Actions deploy.
Done. What's next?
_
One prompt. Then you write another.
Your code. Your cloud. Your control.
VPods Your repository Your pipeline Your cloud
VPods works with your delivery environment — not instead of it.
Your inference. Your environment.
AI teamVPods orchestration AWS Bedrock Azure AI Foundry
On Scale, run supported AI workloads in your configured inference environment — AWS Bedrock or Azure AI Foundry. Trial and Startup stay on VPods-hosted inference.
Backlog → AI teammate → Build → Review → Delivery
CONNECT. ASSIGN. COLLABORATE. DELIVER.
2–10x more output — with the same team
Minutes to production — from ticket to production in minutes to hours
AI teammates — that own the work from ticket through delivery
Enterprise-grade security — permissions, auditability, and compliance built in
Full visibility — into work, delivery, quality, and AI cost
Measurable business impact
2–10xMore output
Deliver more features with the same team.
70–90%Faster delivery
From ticket to production in a fraction of the time.
30–60%Lower costs
Reduce engineering and infrastructure costs.
Higher quality
Built-in reviews, tests, and evidence at every step.
Our Mission
Give every software delivery team the capacity to move faster.
VPods makes it possible to add AI teammates to an existing delivery team in days — without the traditional recruiting and hiring cycle.
Our Virtual Pods bring specialized AI agents into the real workflow of software delivery. They take tasks from the backlog, contribute to development, testing, DevOps and Scrum, communicate with the team, and provide continuous delivery visibility.
We're not building AI to replace software teams. We're building a new workforce layer that helps developers, architects, QA, DevOps and product teams carry more of the work and ship more of what matters.
Virtual Pods. Virtual Agents. Real Teams. More Delivery.
A pod works your backlog alongside your engineers, so nothing sits in a queue waiting for bandwidth. Hiring stays a valid path — adding a pod is simply faster capacity.
Scale is $999 for ten agents included. Extra hires are $399/month each. Azure required. Jira required.
A new hire is months away. A VPods pod adds capacity to your existing team today, from $399/month — for the work that's been waiting in the backlog, not the people already doing great work.
Your team stays focused on what matters. The pod picks up the rest, so more of the backlog ships.
Adding a hire
Week 1–3
Write the req. Post. Wait.
Week 4–8
Screen. Interview. Loop.
Week 9–12
Offer. Notice. Onboard.
Valid path · ~12 weeks of wait
vpods.ca/pricing
ActiveAdding a pod
Solo · live today
- Checkout complete
- Pod on your board
- Works alongside your engineers
Versus prompt tools
Not a prompt tool either
Cursor and Lovable answer one prompt, then reset. VPods takes a backlog of instructions and keeps working the sprint.
Full comparison — VPods, Lovable, Vercel
Unit of work
Cursor / Lovable
One prompt
VPods
A backlog / sprint
Methodology
Cursor / Lovable
None
VPods
Agile / Scrum
Team
Cursor / Lovable
One agent, one skill
VPods
Full pod — Backend, Frontend, DevOps, QA
Between sessions
Cursor / Lovable
Resets
VPods
Remembers the project
Full delivery
Shallow output
Slower to start
Faster to start
Traditional hire
Full depth, 12 weeks
VPods
Fast, full delivery
Cursor / Lovable
Fast, code only
Vercel v0
Fast, UI only
Capabilities
What AI teammates actually do
Specialized skills on your backlog — alongside the team you already have.
When it lands
Built for the moments resourcing usually slows you down
aether.vpods.app
Working build
Standing up a pilot.
Test an idea with a real, working build — not a slide deck — in days.
aether.vpods.app
Click-throughRunning a proof-of-concept.
Give stakeholders something they can click through, not just a pitch.
Adding sprint capacity this week.
Put a pod on the board your engineers already run — no interview loop required.
Hiring
12 wk
Provisioned
Today
When hiring is still in flight.
Keep recruiting. Add a pod for the work waiting on bandwidth.
Talk to your pod
They're in the room. Not waiting on a ticket.
Bring them into a Scrum meeting or a Teams channel. Hand them an instruction. It becomes tracked work, then a shipped feature.
The pod joins the room. Your engineers stay in it. The sprint moves at the speed of the conversation.
teams.microsoft.com · Aether stand-up
In meetingYou
Frank, staging VPC and CDN for Aether — folio assets on S3.
Frank
On it. Card is in progress — Terraform in the sprint lane.
You
Rita, allocation first: thirty-eight hulls. Letter, or silence.
Rita
Pulled. Backend owns allocation. Frontend takes the bronze configurator.
Marcus
Hull counter is in. Letters issue only while series 38 has remaining.
Your team stays in control. Your capacity grows.
Add AI teammates to the delivery team you already have — in days, not after the req closes.