Trustworthy AI
Jaxon mathematically proves the output from LLMs is accurate. With a formal reasoning system, Jaxon ensures AI is predictable for use cases where the accuracy has to be trusted.
Rigorous Fact Checker with Domain-Specific Guardrails
Policy Rules Guardrail
An AI guardrail that turns complex policy docs into structured logic.
Discover How
An AI-powered ‘Fact Checker’ to address the hallucination problem.
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Custom AI applications powered by Jaxon.
Explore ServicesAddressing the Hallucination Problem
The propensity to “hallucinate” is inherent in LLM architectures. Jaxon’s proprietary Domain-Specific AI Logic (DSAIL) facilitates the formal expression of domain knowledge, constraints, and assertions, acting as a bridge between natural language and the structured language computational tools required for mathematical solving. Fact check LLM output with DSAIL to ensure accuracy.
- Assertions - Explicitly stated truths
- Constraints - Rules or limits that define the scope of possible solutions
- Facts - Predefined, true pieces of information the system uses as a base for reasoning and decision-making
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Featured News Stories
Jaxon teams up with IBM watsonx in battle against AI hallucination
Jaxon awarded Phase II SBIR contract with U.S. Air Force
Latest Blog Posts
Art in the Age of Instant AI: What Do We Owe the Originals?
GPT-4o is the internet’s latest obsession – and rightly so. With just a prompt
AI Doesn’t Understand, It Predicts: The Symphony of Sequences
AI hallucinations occur because large language models (LLMs) like GPT generate text by predicting
The Power of Semantic Chunking in AI: Unlocking Contextual Understanding
In the evolving world of AI, semantic chunking has emerged as a powerful technique
