Optica Labs offers a continuous testing, evaluation, verification, and validation platform that ensures AI agents, productivity copilots, and autonomous enterprise systems behave reliably in real‑world scenarios. By simulating operational contingencies and measuring compliance, the service helps organizations avoid costly failures, addressing the $12 billion lost annually to failed AI projects and the projected $4.6 billion in regulatory fines for 2024.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional security, QA, and compliance tools are designed for deterministic software and therefore miss emergent, non‑deterministic behavior of modern AI copilots, workflow agents, and autonomous enterprise systems. Static tests cannot capture real‑world user interactions, leading to guardrail drift and a lack of execution‑level evidence for regulatory compliance.
Solution
Optica Labs offers a continuous AI assurance platform that tests, evaluates, verifies, and validates AI agents throughout their lifecycle. The service simulates real‑world personas, adversarial scenarios, and policy‑circumvention chains in isolated sandboxes, exposing behavioral flaws before they reach production. It provides pre‑build risk scoring, pre‑deployment failure prediction, and post‑deployment guardrail‑erosion monitoring, delivering audit‑ready evidence for regulators and auditors. Integrated modules such as the AI Test Navigator, Persona Simulation Engine, and TEVV Assurance Engine generate quantitative risk metrics, compliance documentation, and actionable remediation guidance. By delivering proactive, end‑to‑end assurance, the platform helps organizations avoid costly AI project failures and regulatory fines.
Target Audience
Primary customers are AI product teams building productivity copilots and autonomous agents, as well as regulated enterprises—such as financial institutions, government, and defense organizations—that require continuous assurance, auditability, and governance of deployed AI systems.
Features
- AI Test Navigator: threat database that recommends test scopes, organizes frameworks, and supports audit preparation
- Persona Simulation Generator: automated 24/7 adversarial and non‑adversarial testing across multi‑agent and social‑engineering scenarios
- TEVV Assurance Engine: behavioral reliability validation layer that sits between models and users for continuous compliance
- Model Identifier with Fragility Scoring: mathematical evaluation of how a given LLM or AI system amplifies client‑specific risks
- Guardrail Erosion Velocity: real‑time measurement of how quickly AI guardrails degrade in production
- Model Robustness Index: cumulative risk scoring that quantifies model drift and overall robustness
- Ingest AI System Context: interactive mapping of organizational goals and risk tolerances into measurable test criteria
- Compliance Traceability: automated generation of audit‑ready evidence linking test outcomes to regulatory frameworks