Inari - Transform customer feedback into your next best product

What

Inari is an AI-native customer feedback analytics and product backlog platform. It is designed for product teams, research teams, CX, sales, growth teams, and founders that need to turn large volumes of customer feedback, sales conversations, and backlog items into structured product insights and prioritization inputs.

The core workflow is to collect feedback from connected tools or uploaded documents, automatically extract themes such as requests, defects, praises, and learnings, and link those insights to customer quotes, company context, and backlog decisions. Based on the page, Inari is positioned as a product operations and voice-of-customer system focused on reducing manual synthesis work and improving prioritization with customer and go-to-market context.

Features

• Automated feedback analysis: Extracts requests, defects, praises, sentiment, and learnings from feedback and sales conversations to reduce manual review work.
• AI-clustered customer insights: Groups feedback into trends and themes so teams can monitor recurring issues and emerging opportunities more efficiently.
• Quote-backed insights with citations: Links insights to relevant customer quotes and supporting metrics, which helps teams validate findings before acting on them.
• AI-supported backlog generation and prioritization: Generates issues, prompts, PRDs, and prototypes, then helps prioritize backlog items using feedback and CRM context.
• Backlog sync and enrichment: Syncs existing backlogs from tools such as Jira and Linear and enriches them with customer and GTM context.
• Customer and company analytics: Aggregates requests, insights, and feedback by account and syncs with HubSpot and Salesforce to support product prioritization and follow-up.

Helpful Tips

• Evaluate how well the platform’s extracted themes match your team’s existing taxonomy before rolling it out broadly; customization appears to be an important part of accuracy.
• Use a defined feedback intake process across support, sales, research, and product so the analysis layer receives consistent and high-signal inputs.
• Verify how AI-generated PRDs, prototypes, and prioritization outputs fit your internal product development standards; these outputs are useful accelerators but should still be reviewed.
• If revenue impact matters in prioritization, confirm that your CRM data quality is strong, since backlog ranking will only be as reliable as the GTM context attached to it.
• For teams comparing tools, pay close attention to citation quality, editability, and backlog workflow fit rather than only summary speed.