🦊 Inari: AI-Powered Customer Insights Hub

TL;DR

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 (frank@useinari.com)!

The Problem(s)

• Product, design, and engineering teams rely on feedback and research data for insights and empathy for building products people love. However, this data lives across several sources like Salesforce, Zendesk, customer interviews living in PDFs, and data sitting in CSVs so it’s tough to maintain a 360-degree view of opportunities to improve customer experience.
• It’s time consuming to manually sift through, analyze, and tag customer feedback data to be used for roadmap and feature planning. Having to deal with and endless amount of copying and pasting feedback + fiddling with context limits in ChatGPT and Claude is also mind-numbing. We had several designers and UX researchers come inbound saying they have to sift through 100+ pages of customer research transcripts for themes 🤯.
• It’s hard to know the exact volume of customers that are mentioning a specific problem or topic and having this data on hand is critical for sizing magnitude for problems when deciding what features or themes to prioritize.

How It Works

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.

Founder Background

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.

Our Asks 🙏🏼

• If you know any teams struggling with extracting takeaways from their feedback data, whether it’s thousands of structured feedback from Zendesk or a few 10 page customer interview transcripts, send them our way and we’d love to help them out! Reach us at frank@useinari.com or eric@useinari.com.
• We’d love any feedback! Especially around how your team is currently managing feedback and roadmap items, any frustrations, and which datasets you need handled.