Chattermill
AI-native feedback analytics across every support channel
About
Chattermill is an AI-native customer feedback analytics platform for teams drowning in unstructured support data: tickets, chats, calls, surveys, app reviews, and social. Its Lyra AI engine does aspect-based sentiment analysis with phrasal granularity across 99+ languages natively — no translation step — and links themes to business metrics like NPS, CSAT, and CES. It ships 90+ native integrations (Zendesk, Intercom, Salesforce, Dixa), anomaly detection with Slack/Teams alerts, speech analytics, social CX analytics with competitor benchmarking, and an MCP server so AI agents can query feedback data directly. G2 rates it 4.4-4.5/5, and customers include HelloFresh, Booking.com, and Uber per the company. Pricing is custom and volume-based, which keeps it opaque, and it analyzes feedback rather than collecting it — you still need survey and ticketing tools upstream.
What it can do
- Aspect-based sentiment analysis; 99+ language NLP; theme clustering; anomaly detection; speech analytics; social CX analytics; MCP server; Slack/Teams alerting.
Setup
Connect feedback sources (tickets, surveys, reviews, calls), configure themes and metrics mapping, set up alerts and dashboards.
Prerequisites
Existing feedback collection tools; volume of unstructured feedback worth analyzing.
Use cases
Works with
Trust score — 7/10
Scored 7/10 (verified tier, metrics-v1). Evidence: Lyra AI engine, aspect-based sentiment, 99+ native languages, 90+ integrations verified on chattermill.com comparisons; G2 4.4-4.5/5 (~237 reviews); customers HelloFresh/Booking.com/Uber per company; MCP server for AI agent querying documented; Limitation: custom volume-based pricing opaque; analyzes feedback but does not collect it.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.