Evidence is a first-class object.
Prism groups recurring customer problems into patterns, but every pattern remains inspectable. A PM can open it, see representative customer language, understand how many conversations support it, and challenge the synthesis before acting on it.
The goal is not a black-box summary. It is faster synthesis without losing the customer voice.
From raw conversations to opportunity.
The PM uploads a structured CSV, reviews recurring patterns, inspects supporting quotes, converts a meaningful pattern into an opportunity, then adjusts customer demand, strategic importance, and engineering effort.
Prism recalculates the priority live and explains the recommendation, confidence, and assumptions behind it.
AI surfaces demand. PMs supply judgment.
AI normalizes, clusters, summarizes, and drafts opportunity statements. The PM decides whether a cluster represents a real product problem, how much weight to give frequency or severity, and what strategic or engineering context changes the decision.
Adjustable inputs deliberately replace a fixed “magic” prioritization formula so the tool supports exploration rather than false precision.
A decision someone else can understand.
The prototype is successful if PMs reach defensible decisions faster, can trace high-priority opportunities back to real customer evidence, and understand how changing assumptions changes the outcome.
Someone who did not perform the original analysis should still be able to understand why an opportunity was prioritized.