Web Test Automation
PlaywrightCypressSelenium WebDriverWebdriverIOPuppeteerTestCafe
Quality is our architecture of success, that translates into customer trust.
Enterprise Quality Engineering That Starts at Sprint One
Quality assurance inserted at the end of a development program does not produce quality software. It produces software with documented defects and a compressed timeline for fixing them. OpenTeq's Quality Engineering practice embeds quality at every stage of the software development lifecycle, from requirements validation through architecture review, continuous automated testing, and production monitoring.
Building Software Quality into Every Stage
Quality Is Not a Gate at the End. It Is the Architecture of Everything Before It.
Most technology programs treat quality as a final checkpoint, a review that happens after the build, a test cycle squeezed into the last two weeks of a sprint, a sign-off that happens before go-live and nothing more. At OpenTeq, quality is an engineering decision made at initiation and honored at every stage that follows. It is defined in the architecture. It is planned into the scope. It is executed in the sprint. It is measured at every milestone gate and it carries forward into how the solution operates in production, long after delivery is complete.
Our OQ Standard governs quality across three dimensions simultaneously: Quality of Outcome ensures every deliverable performs against the business requirement it was built for; Quality of Intelligence ensures the solution improves with every operational cycle, every data input, and every user interaction; Quality of Trust ensures the security, compliance, and governance controls your environment demands are embedded by design not added as an afterthought.
The result is a delivery model where quality does not slow velocity it is what makes velocity sustainable. Every release that leaves our teams is operationally qualified, production-proven, and built to compound in value from the moment it goes live.
Cloud-Native Engineering
Quality Engineering Services
Consciously embed quality from day one with OpenTeq's AI-first engineering practice, delivering continuous quality assessment across reliability, performance, and security throughout your entire Agile delivery lifecycle.
Test Automation
Adopt intelligent, scalable automation frameworks that leverage AI prediction capabilities to accelerate testing cycles, improve coverage, and surface quality metrics in real time, giving your teams rapid, data-driven feedback at every sprint.
Functional Testing
Experience structured functional validation aligned with Agile delivery rhythms, consciously verifying business logic, user journeys, and acceptance criteria from sprint one, so quality is never an afterthought.
Security Testing
Embed proactive security assurance early in your delivery pipeline with AI-assisted vulnerability detection, penetration testing, and compliance validation, treating application integrity as a continuous quality metric, not a final checkpoint.
Performance Testing
Harness AI prediction capabilities to model load, stress, and scalability thresholds before they become production incidents, assuring your applications sustain best performance under any real-world demand scenario.
API Testing
Validate API reliability, security, and performance continuously across Agile delivery cycles, leveraging automated quality models to maintain seamless integration, consistent data exchange, and measurable contract compliance.
Mobile Testing
Deliver flawless mobile experiences by applying Quality 4.0 approaches across devices, OS versions, and network conditions with continuous quality assessment that surfaces coverage gaps before your users ever encounter them.
Automation Testing
Implement AI-augmented test automation frameworks that consciously support continuous delivery pipelines, enabling measurable improvements in test coverage, cycle time, and release confidence through strong, self-maintaining test suites.
AI Testing
Validate machine learning models and intelligent systems with purpose-built quality models that assess accuracy, bias, performance, and ethical compliance, making sure your AI outputs meet both technical and responsible AI standards.
Big Data & Analytics Testing
Certify data correctness, completeness, and pipeline performance across big data ecosystems using continuous quality assessment and IoT sensor-integrated validation, ensuring your analytics and ETL workflows deliver trustworthy, audit-ready outputs.
Blockchain Testing
Apply Quality 4.0 approaches to test smart contracts, agreement mechanisms, and distributed ledger systems, leveraging blockchain for traceability and integrating continuous quality assessment to validate security, performance, and functional correctness.
Cloud Migration
Consciously de-risk cloud migrations with AI-assisted quality models that continuously assess functionality, performance, security, and data integrity, guaranteeing each migrated workload meets defined quality metrics before and after go-live.
Security Assurance
Apply a comprehensive, AI-first security assurance program that integrates vulnerability assessments, penetration testing, and compliance validation into your Agile delivery lifecycle, treating security as a continuous quality metric from day one.
IoT Testing
Experience end-to-end IoT ecosystem validation powered by Quality 4.0 approaches, integrating IoT modules, AI predictive functions, and traceability mechanisms to assess connectivity, interoperability, security, and performance spanning every device and environment.
Mobile App Testing
Consciously cover every mobile platform, device profile, and network condition with AI-augmented testing strategies, delivering continuous quality assessment that secures consistent, high-performance mobile experiences across your entire user base.
RPA Testing
Validate robotic process automation workflows with precision quality models that assess accuracy, resilience, and exception handling, consciously making sure your automated business processes perform reliably within Agile delivery frameworks.
Enterprise Application Assurance
Adopt a Quality 4.0-aligned assurance approach for ERP, CRM, and custom enterprise systems, integrating continuous quality assessment, AI predictive functions, and end-to-end traceability to safeguard reliability, integration integrity, and uninterrupted business continuity.
Our QE Signature Practice
AI in Testing — Three Categories That Actually Ship to Production
"AI testing" isn't one thing — and treating it as one is why most teams' AI testing efforts stall in pilot. There are three distinct categories of value, each at a different maturity level. Two are production-ready today. One is the most fertile frontier in QE.
Signature Program
Generate · Maintain · Validate — the Real Framework
Tests that author themselves from intent. Tests that maintain themselves when the UI drifts. Tests that validate behavior at a depth humans can't reliably match — especially for GenAI features that don't have deterministic outputs to assert against. That's the framework. The maturity gradient runs left to right.
Our practice combines commercial AI-native platforms (Mabl, testRigor, Functionize, Applitools, Tricentis) with custom LLM tooling built on Anthropic Claude — including a Claude-as-judge eval pattern for GenAI feature validation that goes beyond what most commercial platforms support today.
See AI Testing Reference Architecture→ci-pipeline.log · AI-Augmented Test Run
▶Generating tests from PR-2148 acceptance criteria...
✓14 test cases generated by Claude (4.2s)
▶Executing test suite (412 tests, smart-selection)...
✓checkout.spec.ts · 18 / 18 passed
⚡
login.spec.ts · locator drift detected
auto-healed: #btn-login → [data-testid='login-cta']
✓login.spec.ts · 12 / 12 passed (healed)
✕
cart-summary.spec.ts · visual regression
AI triage: real UI bug · not flake · introduced in PR-2148
▶Filing bug · linking to PR-2148 commit...
▶Running Claude-as-judge eval on chat-assistant...
✓47 / 50 LLM outputs grounded · 3 flagged for review
✓Done. 411 / 412 · 1 defect · 0 flakes · 6.2 hrs saved
01 · PRODUCTION READY
Generate
Tests author themselves from intent — requirements, user stories, or app exploration.
⚡
AI Test Generation
LLM reads user stories or acceptance criteria and writes test cases. Engineers review, tune, and merge.
40–60%Test authoring time reduction
Where it lands: high-volume backlog regression suites and onboarding new product surfaces.
Claude · GPT · Mabl · testRigor
✦
Exploratory AI Testing
Autonomous agents explore the app like users do — clicking workflows, probing edges, reporting surprises.
3–5×More edge cases surfaced vs human exploratory
Where it lands: pre-release exploratory runs on new features humans haven't yet thought through.
Custom Claude Agents
02 · PRODUCTION READY
Maintain
Tests survive UI change and triage their own failures — slashing the maintenance tax.
↻
Self-Healing Tests
Automated locator drift detection. When the UI changes, the test adapts instead of failing.
60–80%Test maintenance burden reduction
Where it lands: long-lived E2E suites on rapidly-evolving web and mobile UIs.
Mabl · Functionize · Tricentis
⌕
AI Failure Triage
Cluster failures across thousands of runs. Distinguish flakiness from real defects. Link failures to root-cause commits.
10×Faster mean-time-to-triage
Where it lands: high-volume CI pipelines where engineers ignore failures because triage is too slow.
Launchable · Custom Claude
03 · RAPIDLY MATURING
Validate
Tests catch bugs and behaviors humans can't reliably check — pixel-level and LLM output quality.
👁
Visual AI Testing
Pixel-level regression with ML noise filtering. Catches layout, color, font, spacing bugs traditional automation misses.
85%+Visual bug catch rate (vs ~30% manual)
Where it lands: design-system-driven products where visual consistency is the contract.
Applitools · Percy · Chromatic
✔
GenAI Feature Validation
Eval harnesses for LLM features. Hallucination detection, grounding tests, prompt-injection resistance, Claude-as-judge scoring.
New CategoryRequired for any production LLM feature
Where it lands: chat assistants, copilots, RAG features — anything without a deterministic right answer.
LangSmith · Promptfoo · Claude
Testing Tools & Platforms
Tooling Depth Across the QE Stack
We operate across the modern QE tooling landscape — commercial platforms, open-source frameworks, and increasingly AI-native testing tools. Pick the right tool for the workload, not the one we happen to resell.
Mobile Test Automation
AppiumXCUITestEspressoDetoxMaestroBrowserStackSauce Labs
API & Contract Testing
PostmanREST AssuredPactKarateNewmanSoapUIHoppscotch
Performance & Load
k6JMeterGatlingLocustLoadRunnerNeoLoadArtillery
Security Testing (SAST/DAST)
OWASP ZAPBurp SuiteCheckmarxSonarQubeSnykVeracodeSemgrep
AI-Native Testing Platforms
MabltestRigorFunctionizeTricentis ToscaACCELQApplitools
Accessibility & Visual
axe-corePa11yWAVELighthouseApplitoolsPercyChromatic
Quality Analytics & Reporting
AllureLaunchableReportPortalXrayZephyrTestRailDatadog CI
Test Data & Service Virtualization
FakerTonic.aiGenRocketWireMockMountebankHoverflyMockoon
Why Choose OpenTeQ for Quality Engineering
AI-Powered Test Generation
Accelerate release cycles with our automatically generated test cases that minimize manual effort.
Self-Healing Test Scripts
Adapt to UI changes automatically with tests that eliminate costly maintenance and reduce flaky failures.
GenAI Validation Expertise
Avail specialized frameworks to validate non-deterministic AI outputs that traditional testing methods can't assess.
Domain-Specific Test Strategy
Make the most of our Industry-aware testing tailored to the compliance needs of Insurance, Banking, Healthcare, and Life Sciences.
Shift-Left Quality Engineering
Experience quality embedded from day one of development, catching defects earlier and reducing rework costs.
Scalable Engagement Models
Optimize with our Talent-as-a-service offering, which provides flexible team structures that scale up or down based on your timelines and budget.
QE Across Industries
Domain-Fluent Quality Engineering
From Insurance to Healthcare to Banking, OpenTeQ Technologies' engineers' quality that meets your industry's highest standards.
BFSI
Banking & Financial Services
- →Core banking regression suites
- →Payment rails performance testing
- →Fraud rule QE & model validation
- →Open Banking API contract testing
Compilance: SR 11-7 · PCI-DSS · SOX ITGC · FFIEC
P&C
P&C Insurance
- →Guidewire / Duck Creek regression
- →Claims workflow E2E automation
- →Underwriting rate engine testing
- →Policy migration data validation
Compilance: NAIC · State DOI · NIST AI RMF
Rx
Life Sciences & Pharma
- →Computer system validation (CSV)
- →Veeva Vault & eTMF testing
- →Clinical trial data integrity
- →Pharmacovigilance system QE
Compilance: 21 CFR Part 11 · GAMP 5 · GxP · EU Annex 11
Rtl
Retail & E-Commerce
- →Black Friday peak-load testing
- →Checkout funnel E2E automation
- →Mobile app QE (iOS / Android)
- →Personalization & search relevance
Compilance: PCI-DSS 4.0 · ADA / WCAG 2.2 AA · GDPR / CCPA
Hc
Healthcare & Providers
- →EHR integration testing (Epic / Cerner)
- →FHIR API contract validation
- →PHI handling & data masking
- →Clinical decision support QE
Compilance: HIPAA · HITRUST · FDA GMLP · 21 CFR Part 11
Mfg
Manufacturing & Industrial
- →MES / SCADA integration testing
- →IoT device telemetry validation
- →Supply chain workflow QE
- →Quality system (eQMS) testing
Compilance: ISO 9001 · IATF 16949 · FDA QSR · CMMC
Hire Expert QA Engineers
Scale with our Talent-as-a-Service model, which lets you hire skilled professionals with expertise in modern testing tools, methodologies, and best practices.
Manual QA Engineers
Performance Specialists
QA Leads & Managers
Automation Engineers
Security Test Engineers
API Test Engineers
Flexible Engagement
Full-time, part-time, or contract
Quick Onboarding
Start within 2 weeks
Vetted Talent
Pre-screened experts
