Product AI

Inspiration

Product Managers sit at the center of product development, yet most of their work involves manually connecting information scattered across tools—support tickets, user feedback, analytics dashboards, feature requests, and documentation. Despite having access to large amounts of data, identifying the right problems to solve and prioritizing the right features often remains difficult and time-consuming.

At the same time, developers recently experienced a massive productivity shift with AI-native tools that assist them while coding. However, Product Managers still rely on fragmented tools and manual workflows.

This inspired us to build Product AI—an AI agentic system designed to act like a Cursor for Product Managers, helping them discover problems, prioritize opportunities, and generate product artifacts faster using intelligent agents.

What it does

Product AI is an AI-powered workspace for Product Managers that transforms scattered product data into actionable insights.

It helps product teams:

Discover recurring user problems from feedback and support tickets
Analyze product data and identify opportunities
Prioritize features using frameworks such as RICE
Automatically generate product artifacts such as PRDs, feature briefs, and product insights

Instead of manually synthesizing information across multiple tools, Product AI uses AI agents to analyze context and assist product decision-making.

How we built it

We designed Product AI as an agentic system composed of specialized AI agents, each responsible for a different part of the product workflow.

The system includes agents such as:

Problem Discovery Agent
Market Intelligence Agent
Feature Prioritization Agent
PRD Generation Agent
Artifact Generation Agent

These agents analyze structured and unstructured product data including feedback, documentation, and analytics signals.

We also incorporated product decision frameworks like RICE prioritization:

[ RICE = \frac{Reach \times Impact \times Confidence}{Effort} ]

This allows Product AI to evaluate opportunities and generate structured product insights automatically.

Challenges we ran into

One of the biggest challenges was handling unstructured data. Customer feedback appears in many forms—short comments, long complaints, vague suggestions—which makes extracting useful signals difficult.

Another challenge was avoiding generic AI outputs. Early versions of the system produced responses that lacked meaningful product context. To solve this, we focused heavily on context grounding and structured inputs.

Designing the agent workflow was also challenging. Determining how different agents should collaborate required several iterations before the system produced reliable results.

Accomplishments that we're proud of

We successfully built a working prototype of Product AI that can:

analyze product feedback
identify problem patterns
prioritize features
generate structured product artifacts

We are particularly proud of designing an agent-based architecture that transforms raw product data into decision-ready insights.

Most importantly, we built a system that demonstrates how AI can augment product thinking, not just automate documentation.

What we learned

Through building Product AI, we learned that product management is fundamentally an information synthesis problem.

The biggest challenge for PMs is not generating ideas—it is discovering meaningful patterns within complex data.

We also learned that agentic AI systems perform better than single prompt-based solutions when dealing with multi-step workflows such as problem discovery and prioritization.

Finally, we learned that context is critical. AI becomes significantly more useful when it understands the product environment it is operating in.