QA as a Service (QAaaS)
Quality isn’t something you buy. It’s something you build.
Ship faster. Release with confidence. Build software people actually trust.
We test conventional software and we test AI systems — and increasingly, our clients need both. Whether it’s a web app, a mobile release, an API layer, or an LLM sitting behind a chatbot, PSSPL brings one quality engineering practice to the job. AI runs through how we work too: it speeds up test creation, keeps automation from breaking every time the UI changes, flags likely defect areas before anyone opens a test case, and gives teams visibility that used to stop at deployment.
What is QA as a Service for AI?
QAaaS means you get quality engineering as an ongoing service instead of building the function in-house. PSSPL delivers both sides of that: we use AI to make testing faster and smarter (self-healing automation, predictive defect analysis, AI-assisted test generation), and we test AI itself — LLMs, GenAI applications, and ML models — for accuracy, hallucinations, bias, and drift, with our testing aligned to the relevant requirements under the EU AI Act, GDPR, and HIPAA. It grew directly out of our core AI development work, so the two practices aren’t bolted together — they share the same engineers and the same delivery process.
From Unreliable Releases to Trusted, Compliant AI
Traditional Software
Most of the QA problems we get called in for come down to a handful of things. Releases break in production because nobody caught the issue in staging — and staging is a lot cheaper to fix than production. Release cycles crawl because regression is still manual, so we automate it and wire it into CI/CD so updates go out in days, not weeks. Customer experience suffers when usability, performance, and cross-device compatibility get tested as an afterthought rather than the way a real user would actually hit the product. Testing budgets get wasted testing everything equally instead of weighting effort toward the highest-risk areas. And for regulated industries, "we tested it" isn't enough on its own — you need traceability, documentation that maps every requirement to a test, and results that are actually ready to hand to an auditor.
AI & GenAI Systems
AI systems bring a different set of failure modes. Structured LLM validation can cut hallucinated or off-target responses by up to 70%. Bias doesn't always show up until you test across protected attributes deliberately, which is why our fairness testing is built around EU AI Act and GDPR requirements rather than added on afterward. Models that perform well at launch can quietly degrade in production — continuous monitoring catches that drift before customers do. On the security side, we red-team for prompt injection, jailbreaking, and data poisoning, the same way you'd pen-test a traditional application. For high-risk AI, explainability (XAI) work matters too, because "the model said so" isn't an answer regulator or your own leadership will accept. And for anything agentic, we test the parts that don't show up in a normal QA checklist: coordination failures between agents, context loss over a session, and emergent behavior nobody designed for.
What we test
QA & Testing Services
Traditional QA & Functional Quality
- Functional testing
- Manual & exploratory testing
- Test automation (Selenium, Playwright, Appium, Cypress)
- API testing (REST, GraphQL, SOAP)
- Performance testing (JMeter, LoadRunner)
- Mobile testing (iOS, Android, PWA)
- Accessibility (WCAG 2.1/2.2, 508, EN 301 549)
- Regression automation for CI
- UAT support
- Release readiness (go/no-go calls)
- Security (OWASP Top 10, pen-test coordination)
- Edge-case and usability discovery
AI & GenAI Assurance
LLM validation & testing – hallucination detection & reduction, context retention & coherence, instruction-following / prompt compliance, safety alignment & content moderation, RAG pipeline validation.
Evaluation & fairness – accuracy, precision, recall, F1; benchmarking against baselines and competitors; comparative evaluation (GPT-4, Claude, Gemini, Llama, custom models); demographic bias detection; EU AI Act readiness assessment.
Security, drift & agents – prompt injection & jailbreak resistance, data poisoning & manipulation detection, drift detection & automated rollback, explainability (SHAP, LIME), AI agent & multi-agent testing.
How We Apply AI in Testing
Quality engineering is shifting from reactive testing to something closer to predictive engineering. AI handles the repetitive parts so our engineers can spend their time on the judgment calls a script can’t make.
A few examples of what that looks like in practice: test scenarios get generated straight from requirements and user stories instead of written line by line.
Automation scripts self-heal when the UI shifts, which has cut maintenance effort by 60–70% on the projects where we’ve applied it.
Visual regression checks compare screens pixel by pixel across devices and browsers. Predictive defect analysis flags the code areas most likely to break before testing even starts, and risk-based regression prioritizes tests by actual change impact rather than running the same suite out of habit.
On the API side, schema validation and anomaly detection run automatically. When something does go wrong, AI-driven clustering helps trace root cause faster, and synthetic, privacy-compliant test data fills in where production data can’t be used. Release readiness analytics tie it together with a predictive quality score to support the go/no-go call.
We start by identifying what kind of AI system we’re dealing with — LLM, ML model, agent, or RAG pipeline — mapping the regulatory considerations that apply (EU AI Act, GDPR, HIPAA where relevant), and agreeing success criteria and risk thresholds up front. From there we build or validate the evaluation dataset, audit training data for quality and bias, and benchmark against baselines with proper statistical rigor (accuracy, precision, recall, F1).
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The LLM/RAG evaluation stage covers hallucination detection and RAG pipeline validation specifically, while safety and security work runs adversarial and red-team testing alongside fairness checks across protected attributes and content-moderation review. Performance and reliability testing covers API and multi-system integration, latency and cost, and scalability under production-like load. Once a system is live, we move into monitoring and drift — continuous checks for input anomalies, data quality issues, and behavioral drift, with automated rollback triggers where they’re warranted. Governance work (XAI implementation, audit-readiness documentation, feedback loops from real incidents, retraining validation) runs throughout, not just at the end.
Achievements and Industry Accolades
Measurable outcomes
Business Benefits
| Benefit | Impact |
|---|---|
| Faster regression cycles | Up to 80% reduction in test execution time |
| Less manual effort | 60–70% reduction through intelligent automation |
| Fewer production defects | 65–75% reduction in post-release issues |
| Lower QA costs | 40–50% savings from automation and risk-based testing |
| Faster releases | 45% faster release cycles within 3 months |
| More release confidence | Predictive quality scoring and release-readiness analytics |
| AI-specific outcomes | Up to 70% fewer hallucinations; up to 98% accuracy achievable |
| Compliance-focused validation | Testing aligned with applicable EU AI Act, GDPR, and HIPAA requirements |
Our Tools & Frameworks
Web automation
Selenium, Playwright, Cypress
Mobile
Appium, plus established native/device tools
API / Performance
REST Assured, Postman, Karate, JMeter, Gatling, k6
CI/CD & quality
Azure DevOps, Jenkins, GitHub Actions, plus established test-management and reporting platforms
AI evaluation
Frameworks our delivery team currently supports for evaluation work; additional tools selected based on your stack
Industry Expertise
Industries we serve
Engagement Models
Why Choose PSSPL
Dual Competence
Traditional software quality assurance and AI system validation, which is an uncommonly found combination.
Quality-First Mindset
Quality is inherent since Day One, rather than being a final step.
AI-Powered Testing
We apply AI to testing and test AI systems (LLM/GenAI validation).
Enterprise automation
Frameworks based on proven practices and scalable.
Shift-left and continuous
Traditional software quality assurance and AI system validation, which is an uncommonly found combination.
Transparent Metrics
Real-time dashboards, readiness scorecards, and no opaque metrics.
Global delivery
Across multiple time zones in India, USA, and Europe.
Domain Compliance
AI Act (EU), GDPR, HIPPA, SOC 2, and other compliance knowledge.
Business Impact
ROI, rather than just test case execution numbers.
Transform Quality Into a Competitive Advantage
Whether you need a dedicated QA team, enterprise automation, AI-powered quality engineering, or validation for LLMs, GenAI and ML systems — PSSPL is ready to be your trusted quality partner. Let’s build software your customers can trust.
Client Success Stories
Frequently Asked Questions
Between a couple of days and up to two weeks, depending on the engagement model that will include knowledge transfer, environment setup, and developing a quality strategy relevant for your product. Project-based engagement can begin in 3–5 business days.
Yes. Our QA engineers integrate into your development, DevOps, and product teams and participate in Agile ceremonies and CI/CD pipeline processes alongside the rest of the team rather than being an outside vendor.
Yes. We develop scalable and sustainable automated test frameworks for web applications, mobile apps, desktop applications, and APIs by leveraging AI-driven engineering and best practices. We also upgrade the existing automation suite if required.
Yes. We analyze the current situation, remove outdated tests, improve coverage, and update frameworks (like Selenium to Playwright and JUnit to pytest, among others), but we don’t lose any business logic embedded in them.
Yes. We can assume full responsibility for your quality engineering operations from strategy and execution to reporting and team management.
For one thing, AI testing needs to consider non-deterministic and probabilistic output, dependencies on data input, model drift, black box opaqueness, and adversarial threats – all of which standard testing scripts will not catch. We leverage data science, statistics, and QA engineering to address the shortcomings of standard QA.
Yes. We employ automated benchmark testing, expert review, adversarial prompt testing, RAG pipeline testing, and multi-turn consistency testing. Our clients usually experience reductions in hallucinations up to 70% post-remediation.
Yes. It will include risk assessment, fairness and bias evaluation, explainability analysis (SHAP/LIME), validation of accuracy and robustness, human-overseeing checks, and documentation ready for an audit.
Continuous performance monitoring, anomaly input detection, behavior drift detection, data quality validation, and automatic rollback trigger upon failure.
Yes. We conduct prompt injection, jailbreaking, data poisoning simulation, edge cases exploitation tests, and generate vulnerability reports.
Evaluation methodologies, labeled data sets, scorecards for models, drift analysis, remediation recommendations, acceptance gates, and governance documentation.
Yes. We test for trajectory correctness, coordination failure, context loss, emergent behaviors, and tool usage.