See through your model's black box
See through your model's black box
How does our system work?
Model Input Image
Klara Analysis
Klara Observations
Correctly focus on ships
Score: 0.8
Low attention usage on water
Uncertainty detected for one ship
Add data with ships at the top of the image
Experiment run on google/vit-large-patch32-384
Klara Data Generation
Automated explainability & error mitigation for neural networks
Automated explainability & error mitigation for neural networks
Github
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Klara Labs
Italy, Genova
davide@klaralabs.com
Klara Labs
Italy, Genova
davide@klaralabs.com
Understand how AI works to create a safe future
Understand how AI works to create a safe future
Our Mission
Our Mission
Used by engineers @ top AI companies
Open your model mind
Input
Data
Score
Critique
Your model
Latent Space
Klara
Engine
Open your models mind
Score
Input
Data
Critique
Your model
Latent Space
Klara Engine
Klara Dataset Generation
How does our system work?
Experiment run on google/vit-large-patch32-384
Model Input Image
Klara Analysis
Klara Observations
Correctly focus on ships
Low attention usage on water
Uncertainty detected for one ship
Add data with ships at the top of the image
Open your models mind
Score
Input
Data
Critique
Your model
Latent Space
Klara Engine
Klara Dataset Generation
How does our system work?
Experiment run on google/vit-large-patch32-384
Model Input Image
Klara Analysis
Klara Observations
Correctly focus on ships
Low attention usage on water
Uncertainty detected for one ship
Add data with ships at the top of the image
Use Cases
Accelerate physical AI deployment while automatically detecting edge cases in production through real-time attention analysis, enabling rapid human intervention when models exhibit misaligned focus or decision uncertainty.
-95% HITL
16x Reliable
Vision-Language Models
Strengthen decision-making reliability by detecting anomalous patterns and model shifts in real-time, enabling timely human intervention when models encounter uncertainty conditions.
-90% HITL
Forecast and Prediction Models
Enhance text generation reliability by identifying reasoning failures and knowledge gaps in real-time, enabling precise human intervention when models produce uncertain outputs or require factual verification.
+95% Accuracy
Reasoning and Language Models
Automate visual safety monitoring in real-world environments by detecting perception failures and unexpected scenarios, enabling instant human oversight when models encounter unfamiliar objects or risky situations.
-80% HITL
12x Reliable
Computer Vision
Use Cases
Accelerate physical AI deployment while automatically detecting edge cases in production through real-time attention analysis, enabling rapid human intervention when models exhibit misaligned focus or decision uncertainty.
-95% HITL
16x Reliable
Vision-Language Models
Automate visual safety monitoring in real-world environments by detecting perception failures and unexpected scenarios, enabling instant human oversight when models encounter unfamiliar objects or risky situations.
-80% HITL
12x Reliable
Computer Vision
Our frontier model can adapt to multiple architecture effortlessly
Enhance text generation accuracy by identifying reasoning failures and knowledge gaps in real-time, enabling precise intervention when models produce uncertain outputs or require factual verification.
+95% Accuracy
Reasoning and Language Models
Strengthen decision-making reliability by detecting anomalous patterns and model shifts in real-time, enabling timely human intervention when models encounter uncertainty conditions.
-90% HITL
Forecast and Prediction Models
Machine Behaviour and Uncertainty
Current AI systems operate largely as black boxes, making decisions without revealing their confidence levels or potential failure modes. This opacity is critical when deploying AI in real-world applications where errors can have serious consequences.
Our goal is to develop a foundational framework that systematically analyzes the inner workings of any AI model. By studying uncertainty and attention patterns, we bridge the interpretability gap, enable informed trust, and implement preventive safeguards. This universal lens into model behavior creates the infrastructure needed for truly safe and reliable AI systems - ones that aren't just powerful, but fundamentally understood and controllable.
Understanding Machine
Behaviour and Uncertainty
Current AI systems operate largely as black boxes, making decisions without revealing their confidence levels or potential failure modes. This opacity is critical when deploying AI in real-world applications where errors can have serious consequences.
We are developing a foundational framework that systematically analyzes the inner workings of any AI model. By studying uncertainty and attention patterns, we bridge the interpretability gap, enable informed trust, and implement preventive safeguards. This universal lens into model behavior creates the infrastructure needed for truly safe and reliable AI systems - ones that aren't just powerful, but fundamentally understood and controllable.
Control panel for your model mind
Explore how Klara makes your AI models more reliable
Understanding Machine
Behaviour and Uncertainty
Current AI systems operate largely as black boxes, making decisions without revealing their confidence levels or potential failure modes. This opacity is critical when deploying AI in real-world applications where errors can have serious consequences.
We are developing a foundational framework that systematically analyzes the inner workings of any AI model. By studying uncertainty and attention patterns, we bridge the interpretability gap, enable informed trust, and implement preventive safeguards. This universal lens into model behavior creates the infrastructure needed for truly safe and reliable AI systems - ones that aren't just powerful, but fundamentally understood and controllable.