What happens when product teams and AI build together?

Natural language has quietly become the most powerful interface between humans and machines. We now talk to AI the way we talk to each other – with conversations instead of commands. Yet most of today’s AI interactions still happen one on one.

You open ChatGPT or Claude, have a conversation, generate something useful, and then share it with your team. Somewhere in that handoff, the context that shaped the AI’s output is lost. The insights, prompts, and reasoning behind the result disappear.

Now imagine a team working together with AI – each member contributing expertise through natural language, and that shared context carried through every step. Until LLMs support true multi-user sessions, we need frameworks that make this collaboration possible. That’s where Rocket Flow comes in.

Rethinking the product development life cycle

At Rocket, our product teams follow the familiar product development life cycle (PDLC). Each member – designer, product manager, engineer, operator – plays a role in turning ideas into delivered value.

We asked a simple question:

What if AI were part of that life cycle, not just a side tool to it?

To explore that, we studied how teams collaborate, how artifacts evolve – from Figma designs to PRDs to system diagrams – and how expertise flows between disciplines. Then we asked how LLMs could amplify that process without losing the judgment, accountability, and creativity that make teams effective.

The answer became our internal framework: Rocket Flow.

Introducing Rocket Flow

Rocket Flow integrates AI directly into the PDLC while keeping domain experts in control. It defines a set of roles – Product Design, Product Management, Engineering Management, Development, and Operations – each with its own specialized workflow.

When you take on a role, Rocket Flow loads the right context, tools, and rules for that part of the life cycle (via MCP servers and system commands). The output from one workflow – say, a PRD – becomes structured input for the next, like a solution design or implementation plan. Context compounds as the work moves forward.

For example, a product manager can feed UX designs and market research into their workflow to generate a complete PRD, reviewed automatically for compliance and risk. A dev leader can then use that PRD to create architecture diagrams and implementation strategies. Developers build from those plans. Each artifact is preserved as source code for knowledge – traceable, reusable, and continuously improving.

Scalable, extensible, and vendor-agnostic

Rocket Flow works with multiple LLMs and developer agents, including Claude Code. It runs inside a team’s own repositories, letting experts define role-specific standards and tools per product stack.

Because every product stack is different – an iOS app doesn’t look like a GraphQL API – Rocket Flow was built to be InnerSourced. Teams evolve their role definitions collaboratively, keeping the framework extensible as their products and practices grow.

What we’re seeing so far

A few teams at Rocket have been using Rocket Flow for several months. Even in its early alpha, it’s showing an order-of-magnitude improvement in speed-to-delivery.

But speed is just the surface. The deeper impact is in the quality and continuity of context – how each AI-assisted artifact captures the reasoning and expertise behind it. Those captured moments of collaboration become raw material for the next wave of innovation.

The future of context-connected work

AI is moving from being a personal assistant to becoming a collaborative teammate. Rocket Flow is our step toward that future — where product development is not just human-led or AI-assisted, but context-connected.

The next leap in productivity won’t come from faster prompts. It will come from teams – and AIs – thinking together.