Jaxon integrates via REST API, making it straightforward to add verification to any existing LLM pipeline. Your application submits text, Jaxon evaluates it against your rulesets, and returns a deterministic result. No rearchitecting required.

Jaxon verifies documents and LLM outputs against policy-based rulesets — checking whether specific conditions hold, facts are supported, and compliance requirements are met. Every rule produces a discrete outcome: pass, fail, or insufficient information.

Thinking models like o3 or Gemini re-interpret your policy from scratch on every call. You get a different “reader” each time — no consistency, no audit trail, no way to prove what logic was applied.

Jaxon encodes your policy once as an explicit, versioned ruleset. Jaxon uses formal logic and a mathematical solver, so verification is fast, consistent, and auditable. It doesn’t matter how the LLM phrases the output — if the underlying facts satisfy the rules, it passes.

Yes. Jaxon is built around an embarrassingly parallel processing model, where documents are validated independently. As demand grows, capacity scales by adding more compute—no reconfiguration required.

Jaxon is particularly valuable in regulated industries — finance, healthcare, legal, and defense — where AI outputs carry real consequences and auditability is non-negotiable. That said, any team deploying LLMs in high-stakes workflows can benefit.

Jaxon was built out of work with the U.S. Department of War, where data privacy isn’t optional. Your data never leaves your infrastructure. Encryption in transit and at rest is standard.