Customer feedback that analyzes itself ✨. Unify your feedback then automatically analyze and generate product insights at scale.
Inari is the AI-powered hub for unifying customer feedback and generating product insights. Instead of sifting through 100’s of pages of user interviews or 1000’s of pieces of customer feedback manually, you can use Inari to consolidate customer feedback across data sources, automatically highlight useful quotes, categorize sentiment, and generate insights. You can use this data to understand what’s on your customer’s minds, figure out which themes will boost engagement and retention, then prioritize your roadmaps.
If any product, UX research, design, support, or other teams want an automated way to analyze customer themes, requests, quotes, and other insights for planning, triaging requests, and other use cases - let us know and we’d love to get this live for you (firstname.lastname@example.org)!
Consolidate feedback in one place: Sales notes, user interviews, Slack requests, CSVs with customer feedbacks... unify them all in Inari. Inari ingests feedback from unstructured and structured sources, consolidates them into a unified hub, then provides a streamlined workflow to manage it all.
Automate feedback analysis: Inari automatically analyzes customer sentiment and highlights top requests, defects, praises, and learnings across your customer feedback and interviews so your team doesn't have to review hundreds of sources manually.
Surface product insights automatically: Reduce churn and build delightful products based on insights, feature requests, bug fixes, and learnings that Inari extracts from connected feedback. Inari attributes insights to the feedback most related to it, so you can size how frequently an issue is mentioned when prioritizing your backlog.
Visualize customer trends: Search, filter, and monitor trends in dashboards built on your consolidated feedback, insights, and customer data.
Check out the quick product demo here.
Frank and Eric met back in 2014 while working together at Amazon. Eric worked @ AWS for 7 years building out Sagemaker, owned their XGBoost image, and deployed algos for the US government before transitioning to full-stack eng for securely managing health and bio data @ Betteromics. Frank worked on ML algorithms an interfaces @ Opendoor between 2016-2020 and managed products like NBA Top Shot + business intelligence teams @ Dapper Labs and Amazon.