hallx started from a simple but uncomfortable truth:
LLMs don’t fail loudly, they fail confidently.
As AI systems move from demos to production, hallucinations aren’t just a model problem anymore. They become a trust, compliance, and brand risk.
hallx is an experimental detection library that identifies when an LLM is likely fabricating information and assigns a real-time risk score before that output reaches users.
What makes hallx different is how it thinks about the problem.
Instead of adding expensive secondary model calls “AI judging AI,” hallx adapts its detection logic to the question itself, whether the model is predicting the future, recalling academic facts, or answering general knowledge.
We built hallx with production reality in mind:
Sub-500ms latency
100% explainable scoring
No vendor lock-in (no evals)
It integrates cleanly with OpenAI, Anthropic, local models, and any LLM provider, so teams don’t have to redesign their stack to ship safer AI. Can you use in existing code systems.
Our belief is simple:
AI adoption won’t be limited by capability, it will be limited by trust.
hallx exists to make that trust measurable, actionable, and deployable at scale.