Jaxon provides an API‑ready verification layer that applies formal logic and executable policy rules to every AI output before a decision is made, ensuring provable correctness and an auditable trail.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying generative AI face the risk of model hallucinations, non‑compliant outputs, and opaque decision making, which can lead to regulatory violations, financial loss, and loss of trust. Without systematic verification, high‑volume AI‑driven workflows require extensive manual review, creating bottlenecks and scaling challenges.
Solution
Jaxon offers an API‑ready verification layer that applies formal logic and executable policy rules to every AI output before a decision is made. By encoding internal policies, regulations, and business constraints as formal rules, the platform provides provable correctness and an auditable trail for each inference. The system integrates with major LLMs (e.g., GPT, Claude, Gemini) or custom models and can be deployed on‑prem, in a VPC, or in the cloud, supporting air‑gapped environments. It shifts human reviewers to the final 10 % of the workflow, preserving speed while ensuring compliance and reducing the risk of hallucinations. Engineers can test and simulate rule performance, compose layered guardrails, and obtain one‑click audits that show exactly which rule failed and why.
Target Audience
Primary customers are forward‑deployed engineering teams in regulated sectors such as financial services, insurance, and life sciences that require rigorous, auditable AI decision pipelines.
Features
- API‑first enforcement engine that evaluates AI outputs against formal, executable policy rules
- Formal logic backend delivering mathematically provable correctness and deterministic results
- One‑click audit trail exposing rule violations and reasoning for each decision
- Chainable guardrails allowing multiple policy layers to be composed into verification pipelines
- Deployment flexibility: on‑premises, cloud, VPC, or air‑gapped environments
- Compatibility with leading LLMs (GPT, Claude, Gemini) and custom model endpoints
- Pre‑deployment simulation tools to test rule performance and impact before go‑live