Checks every AI answer against your rules before anyone acts on it.
Turn policy into enforceable rulesHow every output gets checkedConnect over MCP or RESTChecks a model still meets your standard after it changes.
Define what a correct output isHow a spec becomes an alertCompare model versions before releaseWe build and run the verified workflow when you lack the team.
What Jaxon builds for youFrom your use case to launchGuardrails, proof and human reviewThe same engine over MCP and REST, for chat and code.
Install and run DSAIL on your own infrastructure.
Getting started (documentation site)System requirements (documentation site)DSAIL language reference (documentation site)REST API (documentation site)Release notes (documentation site)How a ruleset is built, what a check returns, who can run it.
From policy text to a compiled ruleset (documentation site)How a check reports its result (documentation site)UNKNOWN is an answer (documentation site)No model in the loop (documentation site)Authentication and access control (documentation site)Threat model, access control and audit for a deployment, and the policies behind it.
Data handling (documentation site)Service Level AgreementAcceptable Use PolicyPrivacy PolicyTerms and ConditionsAnnouncements, articles and press releases.
Jaxon Awarded Phase I SBIR with U.S. Space Force (external site)Jaxon is Live on the AWS marketplace! (external site)Jaxon awarded phase III SBIR with U.S. Space Force (external site)Press inquiries: press@jaxon.aiChecks every AI answer against your rules before anyone acts on it.
Turn policy into enforceable rulesHow every output gets checkedConnect over MCP or RESTChecks a model still meets your standard after it changes.
Define what a correct output isHow a spec becomes an alertCompare model versions before releaseWe build and run the verified workflow when you lack the team.
What Jaxon builds for youFrom your use case to launchGuardrails, proof and human reviewThe same engine over MCP and REST, for chat and code.
Install and run DSAIL on your own infrastructure.
Getting started (documentation site)System requirements (documentation site)DSAIL language reference (documentation site)REST API (documentation site)Release notes (documentation site)How a ruleset is built, what a check returns, who can run it.
From policy text to a compiled ruleset (documentation site)How a check reports its result (documentation site)UNKNOWN is an answer (documentation site)No model in the loop (documentation site)Authentication and access control (documentation site)Threat model, access control and audit for a deployment, and the policies behind it.
Data handling (documentation site)Service Level AgreementAcceptable Use PolicyPrivacy PolicyTerms and ConditionsAnnouncements, articles and press releases.
Jaxon Awarded Phase I SBIR with U.S. Space Force (external site)Jaxon is Live on the AWS marketplace! (external site)Jaxon awarded phase III SBIR with U.S. Space Force (external site)Press inquiries: press@jaxon.aiUsing generative AI, synthetic data is artificially created from real datasets, both to expand them in general and to acquire more long tail data points. This lets you augment your machine learning models without being restricted by data collection. However, adding more synthetic data also adds more noise, which leads to poorer models.
Greg Harman, CTO of Jaxon, shares his thoughts on how to balance the tradeoffs between noise and quantity.
