See what's actually happening inside your AI models
Autonomous agents that trace circuits, extract features, and audit neural networks. Compliance-ready reports without needing a research team.
You deploy an LLM. It works. But you can't explain why it works, what it learned, or what it might do next. Regulators want answers. Your users want trust. Legacy XAI tools give you surface-level feature importance scores. GlassBox gives you the actual computational graph.
Point it at any model
Connect your model endpoint. GlassBox supports open-weights models and API-accessible LLMs. No code changes required.
Autonomous deep analysis
Agents run sparse autoencoder extraction, circuit tracing, and attribution mapping. They find the features your model actually uses to make decisions.
Compliance-ready output
Get human-readable audit reports mapping model behavior to interpretable concepts. Flag hidden biases, unexpected circuits, and risk vectors.
Circuit Tracing
Map the computational pathways your model takes from input to output. Understand not just what it predicts, but how it gets there.
Drift Detection
Monitor deployed models for behavioral drift. Get alerted when internal feature activations shift in ways that surface metrics miss.
Regulatory Reports
Generate audit documentation aligned with EU AI Act requirements. Transparent model cards with mechanistic backing, not just performance numbers.
Hidden Objective Scanning
Detect deceptive alignment, sleeper behaviors, and hidden objectives before they surface in production. Built on published audit methodologies.
The black box era is ending
Every company deploying AI will need to prove what their models do and why. GlassBox makes that possible without building a research team.