Available
SLM-Works Feedback Analyzer
Structured insight from customer feedback and NPS responses
Transforms free-text customer feedback into structured product and CX signals teams can prioritize and track.
How it works
Step 1
Read survey/NPS/review text streams.
Step 2
Classify sentiment, urgency, and thematic labels.
Step 3
Extract actionable product requests and churn signals.
Step 4
Publish normalized events to analytics or ticketing systems.
Example
Example input
NPS comments from 4 markets with mixed language and sentiment.
Example output
{ sentiment: 'negative', themes: ['billing', 'mobile UX'], urgency: 'medium', churn_risk: 0.73 }
Key features
- Topic + sentiment multi-label classification
- Feature-request and churn-signal extraction
- Batch pipeline support
- Consistent output for dashboarding and trend analysis
Rollout guidance
- Define taxonomy ownership (product vs CX) early.
- Use weekly validation samples for quality drift monitoring.
Ideal for
FAQ
Can this support multiple languages?
Yes, with proper evaluation datasets per language to ensure stable classification quality.
Want this model in your stack?
We can scope a deployment blueprint, evaluation set, and integration plan for your data and infrastructure constraints.