Sprig

The platform's defining capability is applying AI to the analysis layer: rather than requiring researchers to manually review hundreds of survey responses, watch dozens of session replays, or interpret complex heatmap data, Sprig AI automatically surfaces top themes, anomalies, and actionable insights from each data source.

This AI analysis layer dramatically compresses the time between data collection and insight delivery, making continuous research practically sustainable for product teams.

In-product surveys launched directly within web and mobile applications are Sprig's flagship research delivery mechanism, enabling teams to ask targeted questions at precisely the moment when user experience is freshest immediately after a specific action, upon encountering a particular feature, or when a user is about to abandon.

This contextual timing produces dramatically higher response rates and more accurate feedback than post-session email surveys sent hours after the user experience has faded.

Sprig's AI analyzes incoming responses in real time, generating summaries and identifying emerging themes without waiting for the survey to close, allowing product teams to act on developing insights rather than waiting for analysis cycles.

The AI Recommendations feature represents a qualitative-to-quantitative intelligence bridge: Sprig AI analyzes user behavior signals and sentiment from surveys and feedback studies to generate specific, prioritized product improvement suggestions tailored to the evidence collected.

Rather than leaving product teams to interpret research findings and independently derive action items, the AI explicitly recommends what should be changed and why, grounded in the observed user experience data.