"Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful."
- statistician George BOX

Models are manifestation of real world objects and processes. They inherit a risk by being mere a closer clone of reality. When organisations are moving to AI to make important decisions, it becomes important to assess the risk associated with the Analytics implemented in the system. Probyto can independently validate your models and provide report to understand the risk model have in test conditions.

Data Pipeline Validation

Model Validation

Model Improvements

Model Monitoring

Metrics for model valuation (Return on Models)

Data Compliance

  • Validating data pipelines for data flows, types, formats, frequency to make sure the models run and produce intended results.
  • Compliance of models and data for data privacy and AI regulations
  • Model validation for conceptual soundness, back testing, benchmarking, stress testing and other frameworks for best use of models in business.
  • Existing model improvements and algorithms blending for better and accurate results.
  • Model Monitoring dashboards driven my statistical model performance and evaluation metrics.
  • Business case for using models by creating a customised framework for return on model investments.

Probyto have combined experience of domain knowledge for Data Solutions, Technology, Algorithms and use-case knowledge to help you create more impact with Data. We can help you uncover data sources and integrate them into one platform. We can help you deploy visualisation platforms and build stories for business metrics.


Abhishek Singh


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