Myridius uses EVOQ to turn quality engineering into autonomous systems where agents detect change, assess risk, optimize coverage, and execute validation under human-governed controls.
Myridius redesigns QE for the AI era.
Myridius shifts QE from a human-led service model to an AI-native operating model that supports autonomous execution across the engineering lifecycle.
Locating escaped defects, cycle-time drag, and coverage gaps
Defining where agents execute, where humans govern, and how quality fits delivery
Connecting quality to change, code, data, release, and production signals
Moving from manual testing to autonomous quality systems
Myridius uses AI-native execution to expand coverage and cut manual effort.
Myridius brings AI-generated test cases, self-healing automation, validation loops, and agent-led triage into the delivery model.
Built from requirements, APIs, journeys, and code change
Reducing maintenance as interfaces and workflows evolve
Using delivery and production signals to sharpen coverage
Classifying failures and routing action faster
Myridius runs QE through networks of agents.
Myridius uses swarm-based execution, so QE is orchestrated by agents that perceive change, assess risk, optimize coverage, and execute testing autonomously.
Reading system changes and risk signals continuously
Selecting the highest-value journeys and validation paths
Generating, running, and maintaining tests in pipeline
Classifying failures and supporting governed release decisions
Myridius delivers QE through the Evoq operating layer.
Evoq turns the autonomous QE model into reusable delivery by connecting productized accelerators, orchestration, data, and control.
Converting intent and change into executable test assets
Coordinating planning, execution, validation, and triage
Scaling safe, production-like test data
Centralizing risk, coverage, evidence, and release readiness
AI-native architecture. Built to compound in value.
By combining AI-native execution, swarm orchestration, and Evoq reuse, Myridius increases coverage and confidence while lowering costs per cycle.
Through reuse, automation, and better targeting
Without corresponding headcount increase
Through autonomous execution and agent-led triage
Through reusable quality software delivered through Evoq
AI-first, autonomous quality engineering
that delivers measurable business outcomes.
Architected an end-to-end test automation strategy on a low-code platform with BDD-driven architecture, unifying testing across web, POS, legacy desktop, APIs, databases, and file systems.
All end-to-end testing was manual, and existing tools could not span the brand's diverse platforms, creating testing silos that prevented validation of complete user journeys and slowed release velocity.
Accelerated release cycles through automated end-to-end scenarios, drastically reduced manual testing effort, and validated complete cross-application user journeys from input to transaction.
Led a migration from brittle UI-based Selenium testing to a resilient API-first model across Ecommerce, Order Management, POS, and Finance systems, with full CI/CD integration and unified functional and performance testing.
QA relied on slow, brittle UI-based Selenium tests with duplicated functional and performance suites across integrated systems, producing 24-hour regression cycles, slow feedback, and heavy maintenance overhead.
Cut regression cycles by 92%, from 24 hours to 2, eliminated 100% of duplicated testing effort, and reduced test maintenance overhead by more than 60%, accelerating time-to-market.
Delivered a comprehensive test environment architecture and test data methodology spanning six integrated identity platforms (SAP HR, Workday, Active Directory, SailPoint, OKTA, and ServiceNow) to enable end-to-end IAM testing.
The enterprise could not perform end-to-end identity lifecycle testing because application-siloed environments prevented cross-system validation and lifecycle-representative test data was unavailable, leaving complex identity workflows under-tested.
Established a strategy projected to improve software quality through expanded test coverage, reduce test data creation time and manual intervention, and lower production risk by enabling pre-deployment identity validation.
