AI-Powered Data Integration at UserVoice

A large focus at UserVoice has been building out functionality that enables better analysis of data for our customers. We have been using LLMs to help power a variety of new features that provide intelligent insights and automation.

One of the key features I helped build is Impact Reports, which provide summary and insight data based on user-selected lists. These reports use LLM processing to analyze feedback and extract meaningful patterns and trends, helping customers understand the impact of their product decisions.

I also helped develop suggested labeling functionality that provides users with relevant label categorization for their data. This AI-powered feature analyzes feedback content and suggests appropriate labels, significantly reducing manual categorization work and improving data organization.

Another major initiative was building automatic feedback data ingestion and analysis through third-party data integrations. This system uses webhooks to extract relevant feedback from external sources and leverages LLM processing to analyze and present important information to customers. This enables customers to get insights from feedback across multiple platforms in one place.

All of these features are built with a focus on performance, accuracy, and user experience, ensuring that the AI-powered insights are both valuable and reliable.