Unlock Transparent Decision-Making

Explainable AI

Understand your AI's reasoning, ensure trustworthiness, and communicate model predictions to business stakeholders.

Understanding predictions is impossible, or is it?

Deciphering AI predictions can feel like a daunting task. A recent survey revealed that 85% of ML practitioners believe explainability is crucial for ML adoption and trust. With explainable AI features, the complex becomes clear, offering insights into the model’s logic.

A simple explanation

Understand why a decision was made

  • Discover which features impact your predictions the most and why.
  • Ensure that the model’s results and outputs are trusted with easily explainable model predictions.
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Explainability for the whole team

Be prepared with answers for predictions that prompt questions

  • Prevent issues like bias and drift in the future.
  • Easily communicate model results to key stakeholders.
  • Using XAI, you can also simulate “What If” scenarios to see how they affect your model.

Debug your models

Save time explaining production data

  • Analyze how your models reach their predictions.
  • Understand feature impact to characterize model accuracy, fairness, and transparency.
  • Use our Data Point Explainer to debug your data at a specific point, and then re-explain in one click.
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“In a space that is developing fast and offerings multiple competing solutions, Aporia’s platform is full of great features and they consistently adopt sensible, intuitive approaches to managing the variety of models, datasets and deployment workflows that characterize most ML projects. They actively seek feedback and are quick to implement solutions to address pain points and meet needs as they arise.”

Felix D.

Principal, MLOps & Data Engineering

“As a company with AI at its core, we take our models in production seriously. Aporia allows us to gain full visibility into our models' performance and take full control of it."

Orr Shilon

ML Engineering Team Lead

“ML models are sensitive when it comes to application production data. This unique quality of AI necessitates a dedicated monitoring system to ensure their reliability. I anticipate that similar to application production workloads, monitoring ML models will – and should – become an industry standard.”

Aviram Cohen

VP R&D

“With Aporia's customizable ML monitoring, data science teams can easily build ML monitoring that fits their unique models and use cases. This is key to ensuring models are benefiting their organizations as intended. This truly is the next generation of MLOps observability.”

Guy Fighel

General Manager AIOps

“ML predictions are becoming more and more critical in the business flow. While training and benchmarking are fairly standardized, real-time production monitoring is still a visibility black hole. Monitoring ML models is as essential as monitoring your server’s response time. Aporia tackles this challenge head on.”

Daniel Sirota

Co-Founder | VP R&D

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