SignalTrail
Turning Customer Conversations into Product Decisions
SignalTrail is an AI-powered customer intelligence platform that transforms customer conversations into structured insights, helping teams uncover trends,...
Problem: Customer insights are often buried across hundreds of meeting notes and transcripts. Teams spend hours manually reviewing conversations, and valuable patterns are frequently missed. As a result: feature requests are difficult to prioritize, customer pain points remain fragmented across teams, product decisions rely on anecdotal feedback, and Customer Success and Product teams lack a shared view of what customers are consistently saying.
Solution: I designed and built SignalTrail, an AI-powered platform that automatically synthesizes customer conversations and converts them into structured, actionable signals. The platform aggregates transcripts and notes, detects recurring themes, and surfaces the highest-priority insights in a centralized Signal Board, complete with supporting quotes and meeting references. This enables teams to move from individual conversations to evidence-backed product decisions.
My Contribution: Framed the product problem and designed the end-to-end workflow. Defined how signals should be extracted, grouped, and prioritized. Built the application using Lovable and AI-driven workflows. Designed the user experience and Signal Board interface. Developed prompt logic for identifying recurring themes and preserving supporting evidence. Iterated on the product based on different customer scenarios.
Outcome: SignalTrail demonstrates how AI can reduce the manual effort involved in synthesizing customer feedback while helping organizations make more informed product decisions. By turning conversations into structured intelligence, teams can discover trends faster, prioritize customer needs with greater confidence, and align Product and Customer Success around a shared understanding.
Built using Lovable, AI Agents, LLMs, Prompt Engineering, and Granola.