ML teams spend 80% of their time preparing data, not building models. Most AI projects fail because of data quality, not algorithms. Expanso makes your data AI-ready at the source.
The bottleneck isn't compute or algorithms - it's data
Data scientists spend 80% of their time cleaning, labeling, and preparing data. Actual model development is squeezed into the remaining 20%. Projects take months instead of weeks.
Models trained on batch data perform differently in production. Feature engineering inconsistent between training and inference. Models degrade without explanation.
Garbage in, garbage out. Models trained on biased, incomplete, or low-quality data make bad predictions. By the time you notice, the model is in production.
Ensure data quality, context, and governance before it reaches your ML platforms
Schema validation, type checking, and business rules enforced at the source. Bad data never reaches your training pipelines.
Compute features where data originates. Same transformations for training and inference. Eliminate training-serving skew.
Fresh features available in milliseconds. Support real-time inference without batch delays. Enable production ML at scale.
Track every transformation from raw data to model input. Understand what data influenced which predictions. Enable model debugging and compliance.
Data quality checks run where data originates. Schema validation, null checking, outlier detection happen before data moves.
Define feature engineering in YAML. Same transformations apply in batch training and real-time inference.
Send clean, transformed data to Databricks, SageMaker, Vertex AI, or your custom ML infrastructure.
Fresh features available at inference time. Support real-time predictions without batch feature stores.
See how ML teams ship models 3x faster with AI-ready data at the source.