What we built: Turning user reviews into product decisions with AI-driven clarity

For companies operating large-scale mobile apps, user reviews are a goldmine of insights, but also extremely difficult to process manually. Every day brings comments, complaints, bug reports, and feature requests, and sorting them quickly becomes unscalable.

To address this, we built Review Hunter, a proof of concept that transforms raw user feedback into structured, prioritized, and actionable insights, enabling product teams to move faster and smarter.

The challenge: Feedback exists, but teams can’t hear it in time

The traditional workflow exposed three core issues:

• Slow manual processing, with critical bugs taking days to surface

• No clear prioritization, mixing bugs, UX issues, and suggestions without urgency

• Lost opportunities, as valuable insights were buried under hundreds of comments

The key question was clear: How can thousands of scattered opinions become a reliable, automated decision system?

How we built it: AI that turns the user voice into immediate action

This AI-powered platform was designed to analyze, categorize, and convert reviews into prioritized product tasks. What the solution does:

Automated ingestion

• Direct integration with App Store and Google Play

• Configurable APIs to add new feedback sources

Intelligent processing

• NLP-based sentiment analysis using LLMs

• Category classification: bugs, features, UX, complaints, positive feedback

• Trend and pattern detection at scale

Actionable output

• Automatic issue creation in Jira, Linear, or GitHub

• Smart prioritization based on impact and severity

• Full context attached to every task: review text, sentiment, category, and patterns

It doesn’t just analyze feedback, it understands it, organizes it, and acts on it.

What we validated: Faster, clearer, user-driven product decisions

Review Hunter showed strong potential:

• Significant reduction in time to detect and address critical bugs

• Less manual overhead for PMs, QA, and support teams

• More accurate product decisions based on real user behavior

• Faster iteration cycles driven by automated alerts and prioritization