AI Deployment Assurance
Mythos gives validation to AI workflows across data, routing, agents, controls, and evidence: before deployment, during testing, and as systems change.
The Validation Gap
Enterprise AI is becoming an execution layer. It reads data, routes requests, calls tools, and acts. Most teams put these systems into production before they can prove how they behave. Mythos closes the gap between how fast AI ships and how well it is validated.
Agents and RAG pipelines reach into data, tools, and other systems. Without a clear map of what AI can see and do, exposure stays invisible until something goes wrong.
Models, prompts, permissions, and data sources change constantly. A single assessment can't prove a system is still safe a release later.
Security, compliance, and audit teams need evidence a system was validated, not a claim that someone believes it is fine.
The Platform Pair
Enterprise AI now touches data, routes, tools, and approvals. Mythos pairs system validation with AI behavior testing so teams can review risk, evidence, and release readiness before deployment.
System Layer
Offensive security & evidence engine
Athena maps the system foundation: exposure, routes, permissions, findings, and the evidence teams need for review.
AI Execution Layer
AI assurance & validation engine
Achilles tests the AI execution layer: prompts, retrieval, agents, tools, permissions, approvals, and release gates.
The Assurance Loop
Athena and Achilles feed one continuous cycle — not two separate products.
Exposure
Athena
Evidence
Documented
Behavior
Achilles
Release
Gated
Revalidate
Ongoing
Why Mythos
Independent validation
Tests are scoped to your environment and designed to produce reviewable evidence.
Evidence-first output
Findings are organized for security, compliance, remediation, and executive review.
Built for AI execution paths
Mythos looks at what the AI can see, where requests go, what tools are used, and what actions can occur.
Why Mythos
Enterprises are no longer only asking whether AI can generate useful text. They are asking what the system can access, which tools it can use, where requests go, what evidence exists, and whether humans can trust the next deployment step. Mythos exists to help organizations review AI systems before trust expands.
AI systems often connect to documents, data platforms, tools, APIs, tickets, repositories, cloud systems, and business workflows. Mythos helps map what becomes reachable before access expands.
AI can behave differently under edge cases, prompt injection, stale sources, conflicting instructions, overbroad permissions, or tool-use pressure. Mythos helps test the behavior that matters before release.
A deployment decision should not depend on vague confidence. Mythos focuses on findings, proof, remediation guidance, and retest evidence that humans can review.
Platform Demo
Watch one AI workflow move from assessment to validation to evidence — the loop Mythos runs before release and as systems change.
Markets
Products shipping AI features and agents to customers at speed.
Regulated decisioning, claims, and fraud workflows under audit.
Patient and research data inside AI-assisted workflows.
Mission-critical systems that demand controlled validation.
Start with one AI system. Get independent validation and an evidence pack your security, compliance, and executive teams can use.
Opening the Vault.
Initializing secure validation chamber.
Mythos
