The Problem
Your AI makes a decision. Someone asks why. Your team shrugs.
Let's be honest: nobody fully understands what happens inside these models. Neural networks remain fundamentally opaque. But that doesn't mean we're completely blind.
The question isn't "can we perfectly explain AI?" It's "can we get useful insights about what's driving these outputs?"
What's Actually Possible
We use techniques from mechanistic interpretability to peek inside the black box:
White-box techniques (when you have model access):
- Attention analysis: Which inputs is the model focusing on?
- Feature attribution: What's contributing most to this output?
- Probing: What concepts has the model learned?
Black-box techniques (when you only see inputs/outputs):
- Sensitivity analysis: How do small input changes affect outputs?
- Counterfactual exploration: What would change the prediction?
- Confidence estimation: When does the model know it doesn't know?
What this gives you: Not perfect understanding, but useful intuition. Enough to spot problems, debug failures, and build appropriate trust.
How We Help
We help you make sense of your AI systems:
- Model analysis: Apply interpretability techniques to understand behavior
- Failure mode investigation: Figure out why your model is doing weird things
- Confidence calibration: Know when to trust outputs and when not to
- Interpretable alternatives: Sometimes a simpler model you understand beats a complex one you don't
We won't promise full transparency—that doesn't exist yet. But we can help you see more than you do now.