SignalWeave
Context reconstruction system for fragmented user feedback and signals. Clusters and interprets scattered inputs into structured product insights.
Overview
Product feedback arrives broken into fragments — a support ticket here, a Slack complaint there, a half-formed comment in a survey, a sales call quote. Each fragment is noise; together they often tell a coherent story.
SignalWeave reconstructs that story. It clusters scattered signals across channels, identifies recurring themes, and surfaces structured insights that would otherwise stay buried in disconnected tools.
Approach
The system ingests signals from multiple sources, normalizes them into a shared representation, then uses semantic clustering to group related fragments — even when they use different vocabulary or come from different teams.
Each cluster is summarized with its source fragments preserved as evidence, so insights stay traceable back to the original voices rather than getting laundered into anonymous bullet points.
Tradeoffs
Aggressive clustering finds more patterns but collapses real distinctions; conservative clustering preserves nuance but produces too many small clusters to act on. The threshold is context-dependent and the tool exposes it as a tunable rather than hiding it.
Synthesis always risks losing the texture of individual voices. The system mitigates this by keeping evidence one click away, but the tension between summary and source is fundamental, not solvable.