Unified test automation
Functional, API, performance, and accessibility testing on one platform, so the go/no-go meeting reads one readiness view instead of four tools' worth of partial answers.
Platforms / AI-Driven Quality Engineering
The QE suite that gives your engineers their sprints back
In most engineering organizations, the automation team spends its sprints repairing yesterday's tests: locators break, data drifts, and coverage never grows. VirtueATLAS is our AI-driven quality engineering platform built to end that cycle. It designs, generates, executes, analyzes, learns, and improves across web, API, performance, and accessibility, with AI proposing test repairs for engineer review, so the team's time goes into new coverage and the release call gets evidence instead of anecdotes.
- Design · Generate · Execute · Analyze · Learn · Improve
- Self-healing automation
- No-code API testing
- Integrations: Jenkins · GitHub · GitLab · Azure DevOps · Jira
One platform, six stages: from test design to self-improvement
Coverage that spans the whole delivery lifecycle: design and generate the right tests, execute at scale, analyze every run, and let the platform learn and improve from each one, with every stage feeding the next.
- 01
Design
- Risk-based test design
- UI · API · performance · accessibility
- 02
Generate
- AI-assisted authoring, engineer-reviewed
- No-code API test workflows
- 03
Execute
- Parallel & distributed execution
- One-click runs · performance & load
- 04
Analyze
- Analytics dashboards & evidence
- Failure clustering · coverage gaps
- 05
Learn
- Learns from outcomes & app changes
- Predictive risk · real-user monitoring
- 06
Improve
- Self-healing repairs, engineer-approved
- Sprints go to new coverage
The loop closes on itself: every run improves test accuracy and stability over time, and predictive quality intelligence flags high-risk failures before they execute.
Inside the six stages
01
Design
Risk-based test design across UI, API, performance, and accessibility, so coverage starts where failure would hurt most.
02
Generate
AI-assisted authoring and no-code API workflows produce the tests; an engineer reviews what enters the suite.
03
Execute
Parallel and distributed execution, one-click runs, and performance & load testing on every build.
04
Analyze
Analytics dashboards, failure clustering, evidence capture, and coverage-gap analysis turn every run into a readable result.
05
Learn
The platform learns from test outcomes, application changes, and real-user monitoring, and flags high-risk failures before they execute.
06
Improve
Self-healing repairs are proposed by AI and approved by engineers, so suites get more stable with age instead of more brittle.
Capabilities
No-code API testing
Build and manage REST/SOAP API tests through no-code workflows, so API coverage keeps pace with CI/CD without queueing behind framework specialists.
Self-healing automation
AI detects UI and application changes and proposes script repairs; an engineer reviews and approves each one. Maintenance becomes a review task instead of the team's whole sprint.
Predictive quality intelligence
AI-driven analysis flags high-risk test failures before execution and surfaces defect clusters, coverage gaps, and release readiness, so 'are we ready to ship?' gets answered with data.
Architecture
How it's built
One test model across UI, API, performance, and accessibility, coordinated in a single platform.
Design · Generate
Unified, cross-stack test automation
One test model spanning UI, API, performance, and accessibility across microservices, multi-cloud, and distributed systems, with unified visibility over all of it.
Execute
Parallel & distributed execution
Tests run simultaneously across web, mobile, APIs, and databases with intelligent load balancing and one-click execution on every build.
Analyze · Learn · Improve
Continuous learning engine
ATLAS learns from test outcomes, application changes, and runtime behavior, improving accuracy and stability over time, with dashboards, evidence capture, and real-user monitoring feeding the loop.
Integrations
- CI/CD & DevOps
- Jenkins, GitHub, GitLab, Azure DevOps, Docker, Kubernetes
- Work tracking
- Jira
- Coverage
- Web, API, performance, and accessibility; mobile via device automation
Deployment & data
ATLAS is applied within our test-automation and Test Center of Excellence engagements, and is built for microservices, multi-cloud, and distributed enterprise systems. Frameworks are built in your stack so your team can maintain them; ATLAS accelerates authoring, execution, and self-healing without locking you into it.
Per-tenant data isolation; data-handling terms committed in your service agreement. See Trust & Company Facts.
What the AI does, precisely
- Autonomous
- Parallel execution, failure clustering, and real-time monitoring run without intervention.
- AI-assisted
- Test authoring, self-healing repairs, and predictive risk analysis are AI-proposed and engineer-reviewed.
- Human-decided
- What enters the suite, what gates a release, and what a failure means stay engineering decisions.
In service delivery
ATLAS accelerates our AI-driven quality engineering engagements, from test automation to Test Centers of Excellence, and in the assurance loop it's where security findings become permanent regression packs, so a fixed vulnerability stays fixed.
AI-Driven Quality Engineering services →See it running
A guided session with the QE team that uses ATLAS daily: authoring, execution, and locator repair shown on a working suite, with your stack in mind.