Discover and integrate new relevant features auto-generated
by LLMs and greedy feature engineering algorithms from
200+ public, community, and premium data sources
Trusted by data scientists and data engineers
Automated data source optimizations for ML models:
If properly prompted with context from all relevant external data, an LLM significantly improves the quality of its embeddings for text field in a source.
Open Street Map is an example of graph data source
Thus, if multiple sources with different error distributions are used, their ensemble will have better accuracy. This is similar to a consensus forecast.
If it finds the relevant information, it will automatically add a new search key - in this case, the postal code for each IP. This enables searching through all geo data sources in addition to IP sources.
Schools, restaurants, hotels, supermarkets, etc
Living buldings, business centers, etc
Roads, public transport stops, etc
Gov. offices, post office, police, etc
Public parks, green areas, etc
Stats for different distances (1 km / 3 km / 5 km)
Workweek calendars by countries
Public holidays / Observed holidays
Consumer Price index
Сentral Bank Rates
Currencies and exchange rates
Step by step guide