About BaseModel

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

Working with BaseModel is a two-stage flow:

• Build your foundation model
  Connect and validate your behavioral data, configure the training setup, and train a private foundation model. This becomes the reusable representation layer for downstream tasks.
  
  
• Train and use scenario models (transfer learning)
  Define a task and target, then fine-tune a scenario model on top of the foundation model. Evaluate, iterate, and then run predictions and interpret results. You can use checkpoints to resume training or to branch into new scenario models later.
  

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.