About BaseModel

BaseModel is a private foundation model platform that transforms raw behavioral data into predictive insights — quickly, securely and at scale.

It empowers data science teams to build models of cutting‑edge performance for various ML problems without the need for manual feature engineering or long development cycles.

Why BaseModel?

Data science teams face growing complexity in modeling behavioral data at scale—slowing insights and limiting scalability. BaseModel solves this with an automated, scalable platform tailored for behavioral modeling.

Key Benefits

Exceptional Predictive Power:
BaseModel's universal behavioral profiling and foundation model approach allows it to consistently beat traditional and expert-built specialist models in accuracy and performance, including top global AI systems.
Workflow Efficiency:
BaseModel automates feature creation and maintenance, freeing teams for strategic work.
Comprehensive Data Utilization:
BaseModel seamlessly integrates and interprets information across diverse data sources and modalities, ensuring that all available data contributes to model performance
Rapid Deployment:
With flexible connectivity and configuration options, BaseModel allows to turn idea to results in less than a week.
Explainability:
BaseModel identifies prediction drivers - high-impact data sources, features, events and behavioral patterns - enhancing transparency and trust in model outputs.

How It Works?

BaseModel replaces individual models built from scratch with an efficient pipeline to cover many various use cases:

It connects to your data sources and automatically detects column types and relationships to apply optimal feature extractors.
Using proprietary algorithms (Cleora, emde), it builds universal behavioral profiles and trains a foundation model for event prediction.
Then you fine‑tune scenario models for specific business tasks.

Step by Step

There are three main stages when working with BaseModel:

Building foundation model:
Connect and configure your behavioral data, then train your private foundation model. Optionally tweak parameters.
Training scenario models:
Specify targets and parameters to build models for recommendations, propensity, churn, and more. Evaluate and iterate for the best performance.
Using your models:
Run predictions, score entities, interpret results. Use checkpoints to train and refine further models.

Deployment Options

BaseModel offers flexible deployment methods to suit various environments:

Container Image (Docker):
Deployable on any platform supporting Docker, providing a complete environment with necessary dependencies.
GUI Application (Snowflake):
Available as a native marketplace application, simplifying management and reducing infrastructure requirements.

For a deeper understanding of BaseModel's concepts and capabilities, explore the BaseModel Value Proposition in the Knowledge Base section.