Become an AI-native
Your Product Pod is a governed AI product team for client delivery. Bring one client brief and watch it become a working product, while your senior team keeps the relationship, the judgment and the sign-off.
The sprint request goes to our contact form. Tell us the client brief you want to run and we scope the pod with you.
Or start on FreeOne unit of work
One client brief in. One working product out.
You do not have to rebuild your delivery model to find out whether this works. Pick one brief, run it through a pod in an isolated client workspace, and judge the output the way you would judge any other delivery.
You bring
One client brief
A real scope from a real client: the problem, the audience, the constraints and whatever the client has already given you. No rewriting it into a special format first.
The pod runs
Delivery in an isolated workspace
Research, spec, design, build, QA and deploy run inside a dedicated client workspace, with its own budget and permissions, so one client's context never leaks into another's.
You hand over
A working product and its code
A deployed product, the repository behind it and the decisions that produced it. Handed to the client, or kept under your retainer, on your terms.
How a pod is organized
A person in charge, with execution underneath.
A Product Pod is not a swarm and not a black box. One human owns the direction, the ProductOS Agent plans and routes, specialists do the craft work, and everything that ships returns to the person who owns it.
Human
Product Owner
Sets direction, chooses what the pod picks up, approves each stage and stays accountable for the outcome.
Coordination
ProductOS Agent
Your orchestrator
Plans each stage, maintains the shared product context, routes work to the right specialist and returns stage decisions to the Product Owner for approval.
Specialists
Ship
Deploy Agent
Runs the preflight build, pushes to your GitHub repository and takes the deployment to your hosting and domain.
Product Intelligence
The future closed-loop layer: watching what shipped and returning it to the Product Owner as the next input. Not available today, and not part of what you buy today. Your team runs this step.
What is learned returns to the Product Owner, who decides what the pod picks up next.
The lifecycle
A loop, not a relay.
Every stage reads from the same project memory, so the spec is written against the research that was run and the build follows the design that was approved.
- 01
Brief
One client brief enters the pod as the unit of work, inside that client's own isolated workspace.
- 02
Plan
The ProductOS Agent turns the input into a stage plan and routes each piece to a specialist.
- 03
Research
Evidence, competitors and sources are gathered, logged and attached to the project.
- 04
Spec
Requirements are written section by section against that evidence, not from memory.
- 05
Design
Flows, screens and the design system are drawn from the spec that was just approved.
- 06
Build
Production code is written in the project sandbox, against your stack and conventions.
- 07
Test
QA drives the real browser and the real API, then reports severity-ranked findings.
- 08
Ship
Preflight build, push to your GitHub repository, deploy to your hosting.
- 09
Observe
Your team watches what shipped against the signal that started the iteration.
- 10
Learn
What you learn becomes the next input, and a human decides what the pod picks up next.
Learn becomes the next brief, and the loop runs again. Observe and Learn are run by your team today. The Product Intelligence Agent that would close this loop automatically is on the roadmap.
Decision rights
Who decides, and who does the work.
Agents handle the heavy execution. Experienced humans keep strategy, judgment, review and accountability. Stage transitions are proposed for approval, never taken on their own.
Your people decide
- Strategy, positioning and what the product is for
- Which signal or brief the pod picks up next
- Scope, trade-offs and what is deliberately not built
- Brand, taste and the calls that need judgment
- Whether a stage moves forward, and what merges
- Customer, client and stakeholder relationships
- Accountability for the outcome
ProductOS executes
- Research sweeps, source logging and competitor scans
- Drafting the spec section by section from that evidence
- Design system, flows and screens from the approved spec
- Production code written in the project sandbox
- QA passes across the browser, the API and accessibility
- Preflight build, GitHub push and deployment runs
- Keeping one project memory current across every stage
What changes for the studio
Delivery becomes repeatable without becoming impersonal.
The same lifecycle runs for every client, so the parts that used to depend on who was staffed that month stop depending on it.
More client products per senior team
Agency capacity covers 40 active products and 25 concurrent AI agents in one workspace. The constraint moves from how many builders you can staff to how much senior review you choose to give.
Repeatable delivery, not bespoke every time
Every engagement runs the same lifecycle: plan, research, spec, design, build, test, ship. Your process stops being rebuilt per project and starts being something you can quote against.
Client context stays isolated
Dedicated client workspaces keep each engagement's memory, design system, code and AI budget separate, with client-level usage controls on top.
Review and approval the client can see
Unlimited Review Licenses mean a client stakeholder can view, comment on and approve without buying a seat, and white-label previews, reports and a client portal keep it on your brand.
Clean code handoff
Every product syncs to a private GitHub repository and can be exported. Handing over the codebase at the end of an engagement is a normal step, not a negotiation.
Governance you can show a procurement team
Advanced permissions, audit logs, per-client AI budgets and margin and top-up controls are part of the Agency workspace, so delivery stays accountable at portfolio scale.
Current capacity
What a pod runs on today.
A Product Pod runs on the capacity your workspace already includes. These are the current founding prices and allowances, published in full on the pricing page.
Agency
Start hereFor agencies and product studios.
$1,499per month, founding price
- active products
- 40
- User Licenses
- 15
- Collaborator Licenses
- 30
- monthly AI credits
- 40,000
- concurrent AI agents
- 25
Team
For startups and in-house product teams.
$599per month, founding price
- active products
- 10
- User Licenses
- 5
- Collaborator Licenses
- 10
- monthly AI credits
- 15,000
- concurrent AI agents
- 8
Every plan starts with a 7-day free trial. Allowances and founding prices are the ones published on the pricing page.
Ownership and governance
What the client will ask you
The questions that come up in every handover conversation, answered before the kickoff call.
The code is handed over, not held hostage
Every product syncs to a private GitHub repository and can be exported. A product's history follows a fork, a clone, a new repository or a transfer to another workspace, so handover is a normal step.
A human signs off before anything ships
The ProductOS Agent proposes stage transitions and waits for approval. Agents ask a bounded question when a decision needs judgment, so nothing reaches a client review unreviewed by you.
Clients review without buying a seat
Review Licenses are unlimited on every plan. Client stakeholders can view, comment on and approve invited products, and white-label previews and reports keep the surface yours.
Isolation, permissions and audit logs
Dedicated client workspaces, client-level AI budgets, advanced permissions and audit logs are part of the Agency workspace, so you can answer a procurement questionnaire honestly.
Questions worth asking first
What is a Product Pod?
A Product Pod is your governed AI product team inside ProductOS: a human Product Owner directs the ProductOS Agent, your orchestrator, which routes work to specialist agents for research, requirements, architecture, design, build, QA and deployment. For an agency it is the delivery engine underneath an engagement, while your team keeps the client relationship and the judgment.
Does this replace our delivery team?
No. Agents handle the heavy execution: research, spec drafting, design generation, writing code in a sandbox, QA passes and deployment runs. Your senior people keep strategy, scope, taste, client relationships, review and accountability. The output still needs someone experienced to judge it before a client sees it.
How is client work kept separate?
Each engagement runs in a dedicated client workspace with its own project memory, design system, code, AI budget and permissions. Client-level usage controls and audit logs sit on top, so one client's context does not reach another's.
Can we white-label what the client sees?
Yes. The Agency workspace includes white-label previews, reports and a client portal, and unlimited Review Licenses so client stakeholders can view, comment on and approve without buying a seat.
What happens to the code at the end of an engagement?
It is handed over. Every product syncs to a private GitHub repository and can be exported, and a product's history follows it across a fork, a clone, a new repository or a transfer to another workspace. Deployment goes to the hosting and domain you or the client control.
Is the Product Intelligence Agent available today?
No. The Product Intelligence Agent that would close the loop automatically, watching what shipped and feeding it back as the next brief, is on the roadmap and is not part of what you buy today. Your team runs the observe and learn steps and decides what the pod picks up next.
Do we have to change the tools our studio already runs on?
No, and ProductOS is not a one for one replacement for your IDE, your cloud, your database, your analytics, your SEO tooling or your automation stack. It syncs code to GitHub, deploys to your hosting, and leaves the rest of your stack where it is.
What does the Agency workspace cost?
The Agency workspace is $1,499 per month at the current founding price and includes 40 active products, 15 User Licenses, 30 Collaborator Licenses, 40,000 monthly AI credits and 25 concurrent AI agents. Full allowances, add-ons and the 7-day free trial terms are on the pricing page.
Bring one brief.
Run a single client brief through a pod, in an isolated workspace, and decide from the output rather than from a pitch.
The sprint request goes to our contact form. Tell us the client brief you want to run and we scope the pod with you.