upload_dataset
Upload a CSV dataset for analysis. Pass CSV content as a string.
This is the entry point for the analytics workflow. After uploading,
use apply_column_mapping to map columns, then analyze_sales_data.
Args:
data_csv: CSV content as a string (header row + data rows)
filename: Filename for the dataset (e.g. "sales_2024.csv")
api_key: Your ae_ API key for authentication
Returns:
Upload ID, detected columns with auto-suggestions, sample data, and row count. Cost: 5 credits.
apply_column_mapping
Apply column role mapping to an uploaded dataset.
After uploading, use the suggested_role from upload_dataset response
or provide your own mapping. Required roles: date, revenue.
Args:
upload_id: UUID of the upload (from upload_dataset)
column_mapping: JSON string mapping column names to roles, e.g. '{"Date": "date", "Sales": "revenue", "Units": "quantity"}'
api_key: Your ae_ API key for authentication
Returns:
Mapping confirmation with status=mapped. Cost: 0 credits.
quick_analysis
Upload CSV data and auto-analyze in one step.
Combines upload + auto-column-mapping + analysis. Uses LLM column detection
to auto-map columns, then triggers comprehensive analysis.
Args:
data_csv: CSV content as a string (header row + data rows)
filename: Filename for the dataset (e.g. "sales_2024.csv")
api_key: Your ae_ API key for authentication
Returns:
Upload info + auto-detected mapping + analysis results. Cost: 20 credits.
analyze_sales_data
Run comprehensive sales data analysis — correlations, anomalies, and AI insights.
Args:
dataset_id: UUID of the uploaded dataset to analyze
api_key: Your ae_ API key for authentication
Returns:
Analysis results with metrics, correlations, and AI interpretation. Cost: 15 credits.
forecast
Generate ML ensemble forecast with confidence bands.
Uses XGBoost + Prophet ensemble with decision-driven scenario analysis.
Args:
dataset_id: UUID of the dataset to forecast from
api_key: Your ae_ API key for authentication
horizon_days: Number of days to forecast (default 90, max 365)
Returns:
Forecast with point estimates, confidence bands, and scenarios. Cost: 35 credits.
segment_customers
RFM customer segmentation — recency, frequency, monetary analysis.
Args:
dataset_id: UUID of the dataset containing customer transaction data
api_key: Your ae_ API key for authentication
Returns:
Customer segments with counts, characteristics, and AI recommendations. Cost: 10 credits.