Kinn is an automated product intelligence platform that aggregates customer feedback from sources like Steam reviews, Slack, Zendesk, and Reddit into actionable product insights. It uses AI to cluster recurring issues, surface emerging problems, and provide grounded answers that help teams prioritize what to build next.
Funding
Funding not disclosed
Founders
Product
Problem
Product teams struggle to make data-driven decisions because customer feedback is scattered across multiple channels—Steam reviews, support tickets, Slack conversations, and community forums—making it nearly impossible to manually synthesize. Generic AI tools like ChatGPT and Claude lack access to these closed environments and often fabricate data when they cannot retrieve real information, leading to inaccurate insights and misguided product priorities.
Solution
Kinn provides an automated product intelligence platform that ingests customer conversations from external channels like Steam, Reddit, and support tickets, plus internal tools like Slack and Jira, into a single unified system. The platform uses AI to cluster recurring issues into distinct signals, surface emerging problems before they spread, and flag topics gaining momentum across channels. Teams can ask plain-language questions and receive answers grounded in actual evidence, with exact threads and message counts cited for verification. Every response arrives ready to act on, with suggested bug reports and prioritized fixes that include the supporting data behind each recommendation.
Target Audience
Primary customers are product managers, game developers, and studio leadership teams at gaming and software companies who need to monitor player feedback and customer conversations at scale to prioritize roadmap decisions.
Features
- Multi-channel ingestion from Steam, Slack, Zendesk, Reddit, email, chat, and calls with automatic de-duplication across 1,000+ customer Slack channels and 500 weekly tickets
- AI-powered issue clustering that merges related mentions into single signals, such as grouping 55 separate enterprise billing complaints into one actionable theme
- Real-time alerting that surfaces emerging issues and momentum shifts, like detecting checkout errors rising 8x across three channels
- Evidence-grounded Q&A that cites specific message counts and threads rather than hallucinating answers, with support for sentiment tracking and content translation
- Workflow automations with Jira and Sentry integrations, plus MCP access for custom tooling
- Token-based usage model with unlimited seats on every plan, allowing entire teams to query without per-user costs