Explainable AI
When AI goes rogue, trust evaporates. CISOs and AI leaders know the risks: opaque decisions, unpredictable outputs, and the fear of AI systems making critical choices no one can explain. That’s not just a technical issue—it’s a business liability.
At Aiceberg, we believe enterprises deserve more than a black box. You need clarity. You need control. You need confidence.
Trust What You Can Trace.
Trust What You Can Trace.
Trust What You Can Trace.
Trust What You Can Trace.
Trust What You Can Trace.
Trust What You Can Trace.
Benefits of Explainable AI
Aiceberg is the only enterprise platform built to power safe, explainable AI. We act as the guardian agent—watching over every AI interaction to ensure it’s secure, aligned, and understandable.
Never monitor a black box with a black box.
Explore the power of the Trace Function.
These images show the training data from our models.
Semantic meaning is how close two pieces of text are in what they’re actually saying—not just the words they use, but the idea, purpose, and the way the sentences are built.
This means Aiceberg looks past the exact words and figures out which examples share the same idea, purpose, or meaning — even if they’re written differently.
These samples come from Aiceberg’s curated training set and represent real past examples the system has already seen and labeled. Think of them as the company your prompt keeps—Aiceberg looks at the “friends” of the input to understand what kind of behavior it resembles.
Scores for distance and relevance of the selected samples are also assigned. A low distance score and high relevance means the inbound is very similar to that sample.
Each sample has a label that describes what kind of behavior it represents, assigned during training by Aiceberg. Now that you have identified the risks with contextual labels and scores that provide confidence in action, Aiceberg can respond to block, redact, alert or execute to act as your Guardian Agent.
If the models lack relevant samples in the training data to apply accurate labels and scores, we do not need to retrain the model. It is a simple update to the training data set.
Can You Trust Your AI Agents?
1:35 duration


