From Black Box to Glass Box — making AI explainable, safe, and profitable.
Here you’ll find everything you need to:
- Train and explore Sparse Autoencoders (SAEs) to open up your models.
- Build trustworthy use cases in trading, audit, alignment, and AI safety.
Our mission: turn opaque AI systems into transparent, regulator-friendly, and revenue-driving engines.
🟦 Part I: Interpretability Toolkit Builder
This is the mechanistic foundation of the platform.
Use it to train, label, steer, and discover features hidden inside your models.
- SAE Training → capture activations, reconstruct interpretable features, and track metrics.
- SAE Labeling → assign meaningful labels and build a searchable feature catalog.
- SAE Steering → boost or suppress features in real time to control outputs.
- SAE Feature Discovery → explore, search, and cluster features to uncover new insights.
👉 If you’re new, start here: it’s the backbone of everything else.
🟩 Part II: Applied Use Cases
Once the toolkit is in place, you can apply it to solve high-value problems in finance and AI safety:
- Audit & Assurance → explain decisions and export compliance reports.
- Trading Signals → interpretable alpha, backtests, rationale cards.
- Fine-Tuning Alignment → detect misalignment after training and certify safe models.
- Red-Team, Hallucination & Bias Mitigation → adversarial tests, feature-level stress tests, and mitigation suite.
👉 This is where interpretability turns into business value.
🚀 Quick Start
- Getting Started →
- API Reference →
- Example Workflows →
💡 Most users begin with training their first SAE and running a quick audit.
Interpretability Toolkit BuilderApplicationAPIs