PrisM

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Inspiration

Managing a product from ideation to launch is inherently complex. PMs often spend weeks coordinating teams, analyzing fragmented data, and prioritizing tasks manually, which slows down innovation. We were inspired to build Prism to amplify human decision-making with AI, automating repetitive work while providing structured insights. Our goal was to create a platform that allows PMs to focus on strategy, vision, and creativity, while AI agents handle the heavy lifting of analysis, scenario planning, and workflow orchestration.What if a PM had a team of autonomous AI co-workers each mastering a different phase of the product lifecycle?

What it does

Prism provides a full product lifecycle management platform powered by 14 specialized AI agents across five major workflow categories:

Project & Ideation – Generate product ideas, evaluate market potential, explore best- and worst-case scenarios, and align concepts with customer needs and business goals. This phase helps PMs quickly identify which ideas are strategically valuable.

Requirements & Development – Refine user stories and prioritize backlog items in a structured “Now/Next/Later” framework, ensuring development work focuses on features that maximize impact.

Customer & Market Research – Analyze competitor landscapes and synthesize industry trends into actionable insights, giving teams a clear understanding of market gaps and emerging opportunities.

Prototyping & Testing – Generate test cases, iterate on designs based on feedback, and ensure robust validation of features before launch.

Go-to-Market Execution – Plan GTM strategy, create release notes, and communicate with stakeholders efficiently, making launch execution smoother and more predictable.

Additional features include branch exploration for testing multiple agent outputs, convergence analysis to synthesize insights into actionable recommendations, an interactive strategy graph for visualization, and JIRA integration for automated ticket creation.

How we built it

We built Prism using a modern full-stack architecture:

Backend: FastAPI orchestrates agent workflows, handles API endpoints, and processes contextual feature data. Python 3.x powers agent logic, Pydantic validates data, and environment variables manage credentials securely. OpenAI models were integrated via NVIDIA API endpoints.

Frontend: React 18 + TypeScript for UI, with ReactFlow visualizing agent nodes and execution. Tailwind CSS and shadcn/ui ensure a clean, responsive, and intuitive design.

Workflow Management: Each agent receives context about features, generating structured outputs that feed into subsequent steps. Branch exploration allows multiple scenarios to run in parallel, and convergence synthesis distills results into a single recommendation.

Integration: JIRA integration automatically generates projects, stories, and tasks, reducing manual coordination and aligning outputs with real-world PM workflows.

Challenges we ran into

Coordinating parallel agent execution: Ensuring agents didn’t conflict when running simultaneously was complex.

Actionable outputs: Generating AI outputs that PMs could immediately act on without excessive human refinement.

JIRA integration: Mapping agent outputs to real-world project management structures required careful design.

AI-human balance: Allowing AI autonomy while keeping PMs in control of final decisions.

Accomplishments that we're proud of

Successfully implemented 14 specialized agents covering the entire product lifecycle.

Built a real-time interactive strategy graph to visualize agent outputs and relationships.

Automated JIRA project and ticket creation, reducing manual workflow overhead.

Enabled branch exploration and convergence analysis, letting teams test multiple strategies and synthesize results quickly.

Created a platform that supports full end-to-end product development, from ideation to go-to-market execution.

What we learned

AI can augment human decision-making, improving speed and accuracy in complex workflows.

Multi-agent systems need careful orchestration and context sharing to avoid conflicts.

Visualization of agent workflows is critical for team understanding and trust in AI outputs.

Integration with real-world tools like JIRA is essential for adoption and practical usability.

Clear feature context improves AI output quality, emphasizing the importance of structured data.

What's next for PrisM

Adaptable Model Usage: Learn what model is the most efficient to use for specific queries (usage of PMFs)

Deeper Integrations: Expand beyond Jira; integrate with Figma, Linear, and Slack for a unified ecosystem.

MultiModal Capability: Add other form of inputs to navigate PrisM, such as talking to Agents as if you were talking with team members.

Our long-term goal? To make PrisM the autonomous co-pilot for every product team, turning strategy into execution - seamlessly.

Built With

• auth0
• fastapi
• javascript
• jira
• nemotron
• npm
• openai
• pnpm
• pydantic
• python
• react
• reactflow
• rollup
• shadcn
• tailwindcss
• typescript
• vite