This is like having an always-on digital analyst that reads every customer review, support ticket, social media post, and survey response, then tells you in plain language whether people are happy or unhappy and why.
Manual review of customer feedback is slow, inconsistent, and cannot scale to thousands or millions of interactions. This solution automates understanding of customer sentiment so teams can quickly see what customers feel and which issues or features drive that sentiment.
Integration into the broader Keboola data platform and pipelines (data ingestion, transformation, and analytics) creates workflow stickiness and makes it easier to operationalize sentiment insights across the business.
Classical-ML (Scikit/XGBoost)
Vector Search
Medium (Integration logic)
Model inference cost and latency at high volumes of unstructured text, plus data integration throughput from multiple customer touchpoints.
Early Majority
Unlike standalone customer experience or social listening tools, this offering is likely embedded in Keboola’s end-to-end data operations stack, making it easier for data teams to pipe sentiment scores directly into warehouses, dashboards, and downstream analytics or activation workflows.