Understanding what users truly think shouldn’t require a crystal ball—or an overwhelmed support team buried in spreadsheets. For one of our recent projects, EDV Werke took on the challenge of turning raw feedback into real product intelligence using the power of Large Language Models (LLMs).
The goal? To analyze massive volumes of product and user feedback from multiple channels—social media, surveys, app reviews—and extract actionable insights, fast.
The Challenges
Comments and reviews came in faster than teams could read, let alone analyze them.
Traditional analytics often missed subtle cues and emotional tone hidden in unstructured text.
Time-consuming, inconsistent, and prone to bias.
The Solution
Built using Kedro and AWS tools, ensuring structured, scalable data ingestion.
A sleek Python Flask interface makes the results visual and instantly actionable.
Custom prompts fine-tuned for the client’s products sharpened the LLM’s understanding, ensuring the analysis isn’t just smart—it’s relevant.
The Results
A live sentiment dashboard tracking real-time user mood.
Data-driven signals for product iterations and feature rollouts.
A direct link between user voice and roadmap decisions.
A noticeable increase in customer satisfaction and loyalty.
