Simpli Tag
Auto-tagging and taxonomy normalization for support data quality.
Tag ensures your support data has consistent, meaningful labels. Auto-tag untagged cases, normalise inconsistent tags, detect drift over time, and generate data-driven tag schemas — foundational data quality for AI training.
Simpli Tag provides auto-tagging, tag normalization, drift detection, and schema suggestions for support data.
Configuration
| Variable | Default | Description |
|---|---|---|
APP_PORT | 8015 | Server port |
LITELLM_MODEL | openai/gpt-5-mini | LLM model for tagging |
MAX_TAGS_PER_CASE | 5 | Maximum tags per case |
SIMILARITY_THRESHOLD | 0.8 | Threshold for tag normalization clustering |
CORS_ORIGINS | * | Allowed CORS origins (comma-separated) |
Start the server
simpli-tag serveAPI endpoints
All endpoints are under the /api/v1 prefix.
POST /api/v1/auto-tag
Auto-tag cases using LLM analysis, optionally constrained to a known tag set.
POST /api/v1/normalize
Find and merge inconsistent tags into canonical forms.
POST /api/v1/drift
Detect how tag usage changes across time periods.
POST /api/v1/suggest-schema
Generate a recommended tag schema from case data.
GET /health
Health check.
Next steps
- Integration Overview — Connect Tag to your ticketing system
- Model Selection — Choose the right model for tagging