AI that’s setting new standards in CX intelligence

Aspect-based sentiment analysis (ABSA) identifies sentiment at the level of individual themes within a single piece of feedback, rather than assigning one overall sentiment to the entire response.

Chattermill uses ABSA because customer feedback is rarely single-sentiment — a response can be positive about one aspect of the experience and negative about another.

Measuring sentiment at the theme level, rather than the whole-response level, gives a more accurate and actionable view of what's actually driving customer perception.

Customer data is used exclusively to train the bespoke taxonomy for your organization — trained in isolation, with no crossover into any other client's model.

Training draws on historical data and is limited to verbatim response text. No PII — customer identifiers, contact details, or other personal fields — enters the training pipeline.

The resulting model is exclusive to your organization.

Yes. Chattermill takes a data-first approach to identify the overarching structure of your data analysis model, then collaborates closely with your stakeholders to incorporate your business expertise and define an accurate, useful taxonomy.

In addition, customers can propose new themes at any stage — including renaming, adding, or removing themes.

Chattermill automatically redacts PII from customer feedback at the point of ingestion — before it’s stored or processed.

This includes common formats like email addresses, phone numbers, credit card numbers, and social security numbers, along with custom formats defined by your organization.

Once redacted, the information is permanently replaced and can’t be recovered.