The AI Sentiment Automation Engine helps teams label customer, beneficiary or product feedback at scale. Users bring their own labelled examples, train a domain-aware model and export a scored dataset for dashboards or follow-up analysis.
Feedback data is useful only when teams can read it consistently. Manual tagging is slow and generic sentiment tools often miss local language, product terms or sector-specific complaints. This project lets the model learn from the user’s own examples.
Bulk-label feedback immediately instead of manually reviewing every row.
Retraining allows the model to learn the language used by customers, beneficiaries, or internal teams.
Outputs sentiment, confidence, and probabilities for dashboards, root-cause analysis, and ML pipelines.