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So Gartner says that most of the AI projects we’re currently doing will deliver erroneous outcomes. Now what?

From a chatbot that is plain racist, to a systematic gender discrimination CV filtering solution, we’re starting to see more and more case studies about AI that goes wrong…

…and Gartner is saying that this is only the tip of the iceberg.

Why do your projects have a high chance of failing? And more importantly: what can you do TODAY to prevent it?

Model accuracy is not the only metric you should care about.

In this webinar, Olivier Blais, AI/ML Quality Evangelist…


In my previous blog post, I went over how quality and trust are major challenges in today’s machine learning ecosystem. If you haven’t had a chance to read it, I highly recommend that you do before jumping into this post. It sets the stage wonderfully to understand why we decided to build Snitch AI.

About two years ago, my colleague Olivier Blais was building machine learning models for various companies at our partnered service company, Moov AI.

A problem that would frequently occur with customers is that once they were presented a complete model, they were hesitant to go ahead…


Machine Learning (ML) is a revolutionary tool that many organizations are working tirelessly to adopt into their businesses. In these initial forays into ML over the past few years, we’ve developed techniques that vastly outclass algorithms that took decades to develop. The potential seems unlimited but at the same time, we’ve barely scratched the surface of what will be possible in the coming years.

A challenge that most engineering departments face when adopting machine learning however is one of trust. …

Snitch AI

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

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