Inari

Inari is an AI customer feedback analytics platform that helps product, research, CX, sales, and growth teams turn feedback and conversations into cited insights and prioritized backlog items.

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Overview

Inari is an AI-native customer feedback analytics platform that turns feedback, support conversations, sales calls, and unstructured documents into organized product insights. It is positioned as a tool for discovering customer requests, defects, praises, and learnings, then using that information to inform backlog decisions.

The product combines feedback analysis, a unified voice-of-customer feed, and backlog enrichment so teams can move from raw customer input to prioritized product opportunities. The site also shows support for common team workflows such as CSV, PDF, and DOC uploads, direct integrations, and automated alerts.

Features

Automated feedback analysis

Cluster customer feedback, sales conversations, and support interactions into requests, defects, praises, learnings, and other themes, then explore the results through AI-driven queries.

Citation-backed insights

Each insight is linked back to relevant customer quotes and citations so teams can trace findings to source material instead of relying on unlabeled summaries.

Custom taxonomies and alerts

Create custom insights and issues, set alerts, and send updates through Slack or email when new patterns appear.

AI backlog and issue generation

Turn insights into product work by generating PRDs, prompts, and issue ideas grounded in customer evidence.

Backlog enrichment and prioritization

Connect backlog tools and CRM systems to enrich issues with revenue, company, and pipeline context for prioritization.

Multi-source repository and customization

Unify feedback across uploads and integrations, then personalize analysis with custom prompts or fine-tuning on exemplary feedback.

Use Cases

Prioritizing the product backlog
A product team can ingest support tickets, call recordings, and feedback exports to identify the most common requests and defects before planning the next release.
Monitoring voice of customer
Research or CX teams can run recurring analysis on customer conversations and documents to produce a shared feed of trending themes, quotes, and learnings.
Linking feedback to GTM context
Sales and growth teams can connect CRM context such as revenue or pipeline stage to customer issues, making it easier to judge which problems matter most commercially.
Automating feedback ops
Ops-minded teams can set alerts and automate VoC reports so that new patterns are pushed into Slack or email without manual review cycles.
Centralizing multi-source feedback
Teams with fragmented data can unify Slack, Gong, Intercom, files, and other sources into one repository before analyzing the material with customized prompts or fine-tuned models.

Pros and Cons

Pros

Supports multiple input types, including uploaded documents and connected apps.
Surfaces insights with linked quotes and citations for review.
Covers both discovery and prioritization, not just summarization.
Offers self-serve pricing tiers alongside an enterprise path.
Can enrich backlog items with GTM context from CRM and project tools.

Cons

The public pages do not expose every integration or workflow detail, so some setup and capability questions require follow-up.
Enterprise pricing is not published and requires contacting the company.

FAQ

How does Inari work?

Inari analyzes customer feedback, support conversations, sales call recordings, and related documents, then clusters the material into customer insights backed by quotes and citations.

What pricing options does Inari offer?

The pricing page shows a free trial, a Startup plan, a Growth plan, and an Enterprise option. Startup and Growth are self-serve monthly plans, while Enterprise is listed as "TBD/month" with a "Let's chat" flow.

What data sources can I use with Inari?

Yes. The pricing and integrations pages show support for CSV, PDF, DOC, and DOCX uploads, plus connections to tools such as Slack, Gong, and Intercom.

Who is Inari for?

The product is designed for product, research, CX, sales, and growth teams that want to turn customer feedback into insights, backlog items, and alerts.

Are there any known limitations?

The source materials do not describe every limitation in detail, but the product pages emphasize citations, configurable taxonomies, and personalization through prompts or fine-tuning rather than fully hands-off analysis.

Quick Facts

Category: AI product feedback analytics
Primary users: Product, research, CX, sales, and growth teams
Platforms / inputs: CSV, PDF, DOC, Slack, Gong, Intercom, Zapier, and CRM/project tools
Pricing model: Free trial plus paid monthly plans; enterprise is custom
Source domain: useinari.com
Notable workflow: Analyze customer feedback, link citations, and prioritize backlog items