Envariant
AI interpretability SDK for foundation model builders.
Envariant is an interpretability SDK for foundation models that works in the model's latent space. It addresses the problem of model volatility — hallucinations, drift, and brittle outputs in complex domains where verification is expensive, scarce, or ambiguous — by moving verification upstream rather than relying on data iteration.
The SDK exposes a compact set of primitives: detecting and causally tracing behaviors such as hallucinations or invariant violations, inductive reasoning and programmatic steering of outputs, extracting human-readable principles, and synthesising targeted edge cases. It is designed to provide these capabilities without bespoke engineering.
Envariant is aimed at teams working with foundation models in safety, reasoning, and domain-specific evaluation contexts. The pages do not specify packaging details such as plans, trials, or seat-based or usage-based pricing.
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