Adept Mayar
Autonomous AI-Native QA & Statistical Evaluation Suite
Autonomous Quality Assurance for Non-Deterministic Workloads
Adept Mayar is the enterprise-grade quality assurance and continuous evaluation platform engineered specifically for probabilistic intelligence, compound AI architectures, and autonomous agent swarms.
Unlike legacy test automation tools that rely on fragile DOM locators and rigid string equality assertions, Mayar constructs high-dimensional synthetic test matrices, authors living test suites directly from OpenAPI specs and product requirements, and executes calibrated multi-judge consensus scoring across continuous integration cycles.
With Mayar, engineering teams eliminate silent model regressions, mathematically bound semantic hallucinations, and accelerate release velocity from weeks of manual evaluation to automated sub-hour CI/CD verification.
Four Pillars of Mayar
Automated Spec-to-Assertion Authoring
Mayar parses your product specifications, REST/gRPC schemas, and user journeys to dynamically author resilient, multi-turn test suites that automatically adapt as APIs evolve.
Calibrated LLM-as-Judge Consensus Engine
Eliminate evaluator drift with reference-guided anchoring, pairwise positional bias elimination, and multi-model consensus voting with statistical confidence bounds.
Synthetic Scenario Matrix Generation
Generate tens of thousands of adversarial edge cases, multilingual variations, and boundary perturbations without exposing proprietary training datasets.
Automated Semantic Defect Triage
Instantly isolate whether regression is caused by prompt template modifications, upstream foundation model weight updates, or backend API contract breaks.