From 150K Reviews to Actionable Intelligence

A product organization was sitting on a goldmine it couldn't mine: 150K+ customer reviews, support tickets, and survey responses accumulated over years. The signal was almost certainly in there — which features to build, where customers churned, what delighted them — but it was buried in unstructured text.

Their attempts to make sense of it relied on manual tagging by a rotating cast of analysts. It was slow, inconsistent between people, and perpetually out of date; by the time a theme was tallied, hundreds of new messages had arrived. Product decisions were being made on gut feel and the loudest recent complaint.

They wanted to convert this raw feedback into reliable, structured signals — recurring themes, sentiment, and concrete feature requests mapped to product areas — that product and engineering teams could actually act on and trust.

We built a retrieval-augmented analysis pipeline that ingested the full backlog and every new message. It clustered feedback into coherent themes, extracted sentiment, and linked recurring requests to specific product areas — turning a wall of text into a structured, queryable model of what customers were saying.

Results were surfaced in a dashboard where a product manager could see the top themes at a glance, then drill straight down to the verbatim source quotes behind any number. No black box: every insight was traceable to the words a real customer wrote.

Semantic indexing kept lookups fast and accurate at scale, while a human-in-the-loop review step on the highest-impact categories kept quality high and prevented the model from confidently mislabeling edge cases.