Quality Assurance for your Machine Learning Models

Gain visibility inside your ML models. Ensure that your organization delivers robust and high-quality AI.

free your machine learning model blackboxes

I now have total confidence that my team’s models have the expected behavior once in production.

Olivier Blais

Olivier Blais, VP Data Science, Moov AI

mit media lab
Alloprof uses snitch machine learning model validation tool

Snitch AI Makes ML Model Validation Simple

Trust that your artificial intelligence models are doing what they should and detect problems before they affect your business.

Clear and Understandable Reports

Get full reports, tailored for business stakeholders, with all pertinent validation details that can be preserved for compliance and regulatory requirements.

Model quality reports contain all the details needed to validate the quality, robustness, and durability of your machine learning models.

Data drift reports allow you to validate if you’ve had any significant changes in your datasets since your model was trained.

« In 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms or the teams responsible for managing them »

Detect Bias and Validate your Features

Detect bias in your models ahead of time and make sure that the proper features are being considered when generating a prediction.

Identify data drift as your model is used for real business decisions to ensure that the predictions driving these decisions remain as accurate as possible.

Detect Bias in your models
Anticipate potential problems

Anticipate and Solve Potential Problems

Identify poor labeling, over and under-fitting in your model and understand how well your model will perform when confronted with real-world data.

Quickly detect data leakage, noise sensitivity and vulnerability to extreme scenarios.

Improve your Model's Performance

See opportunities for simplifying your model or pruning features that have little or no impact on model accuracy, allowing you to gain improved performance and reduced operating costs.

Improve your machine learning models' performance

In-place Compatibility

Compatible with any trained TensorFlow model, no need to migrate your entire data pipeline to a new platform. Validate your models from AWS, GCP, Azure or your own environment.
Supports any TensorFlow model
Supports Google Cloud Platform
supports amazon web services
Supports Microsoft Azure Machine Learning Services
We take your data security very seriously


We use industry-leading security best practices to make sure your data stays safe.

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The user experience is simple


You’ll be ready in less than 5 minutes. No complex integration. It just works.

Snitch AI is scientifically accurate


We teamed up with research institutes to ensure the validity and accuracy of our framework.

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Simple plans that fit your needs

All plans includes:

Prices in USD

One Project


Five Projects


Twenty Projects​

Need more than 20 projects? Interested in our on-premises offering?
No problem, book a call!

How does Snitch AI work?

Our machine learning model validation tool takes a trained model, the training dataset and the validation dataset, and performs a series of mathematical validations.

As such, Snitch AI can detect many potential issues with a model that would prevent it from performing at peak efficiency in the desired business scenario. Since underperforming models can directly cause loss of efficiency and increased costs, this can be a major issue.

Robustness vs Accuracy?

Model robustness is a large term that encompasses many characteristics that predict how well a model will perform in real-life scenarios. Typically, when training a ML model, data scientists focus on improving a single metric: accuracy.

Accuracy measures how well the model can accurately perform the right prediction given the training and validation sets. However, this leaves the model vulnerable to a host of other shortcomings.

Our machine learning model validation tool seeks to address these shortcomings by giving visibility to more than just accuracy and allow business stakeholders to deploy ML models into production with confidence.

What does the validation tool evaluate in your models?

  • Data Drift
  • Feature Bias
  • Sensitivity to extreme noise
  • Sensitivity to random noise
  • Over-fitting*
  • Labeling Errors*
  • Data Leakage*
  • Under-fitting*
  • Model Simplification*
  • Feature Discrimination/Pruning*

    * coming soon

What's included in the model validation report?

The report outlines all the outputs described in the previous section and is tailored for business stakeholders. While it contains some raw data that can help Data Science teams pinpoint and fix issues with their model, it will also contain a clear explanation of each of the observations as well as potential impacts that this could have on model performance.

The report is also signed digitally and timestamped. It can be preserved for compliance and regulatory requirements.

With the report in hand, you will be able to confidently deploy your machine learning models into production and ensure the best possible business outcomes from using them.

Still not convinced?

Book a demo with Simon!

Get Started with Snitch AI

Learn everything about your model blackboxes.

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