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 don’t just test your software โ we validate your AI systems too. Whether it is LLMs or other applications of GenAI or even classic enterprise software, PSSPL delivers comprehensive quality engineering services for deterministic as well as probabilistic systems. AI underlies everything: smarter testing, self-healing automation, predictive defect analysis, and deep quality insights beyond just deployment.
About Prakash Software
25+
Years in software engineering
500+
Projects delivered
50+
QA & automation engineers
15+
AI-certified QA professionals
100+
Enterprise automation frameworks
12+
Industries served
Dual QA
QA + AI validation expertise
Global development centers
Offices in USA, UK & Australia
What is QA as a Service for AI?
QAaaS is quality engineering delivered as an ongoing outsourced service โ and PSSPL covers both halves of that equation. The use of AI in testing is made possible by us (AI-based automation, self-healing scripts, and predictive defect analysis), as well as testing of AI (testing of LLMs, GenAI, and ML for accuracy, hallucinations, biases, drift, security, and compliance with EU AI Act, GDPR, and HIPAA). It’s a natural extension of our core AI development practice.
Problems we solve
From Unreliable Releases to Trusted, Compliant AI
What we test
QA & Testing Services
Traditional Software Testing
- 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 Testing
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 compliance testing
Security, Drift & Agents
- Prompt injection & jailbreak resistance
- Data poisoning & manipulation detection
- Drift detection & automated rollback
- Explainability (SHAP, LIME)
- AI agent & multi-agent testing
AI-powered quality engineering
How We Apply AI in Testing
Quality engineering is moving from reactive testing to predictive engineering. AI takes the repetitive work off our engineers’ plates so they can focus on the scenarios that actually need human judgment.
AI-assisted test generation
Comprehensive scenarios generated straight from requirements and user stories.
Intelligent automation
Self-healing scripts that adjust when the UI changes, cutting maintenance by 60โ70%.
Visual regression testing
Pixel-perfect AI comparison across devices and browsers.
Predictive defect analysis
Flags high-risk code areas before testing even starts.
Risk-based regression
Prioritizes tests by actual change impact, not habit.
AI API validation
Automated schema validation and anomaly detection.
Synthetic test data
Privacy-compliant data generation.
Root cause analysis
AI-driven defect clustering and pattern recognition.
Release readiness analytics
Predictive quality scoring to inform go/no-go decisions.
Achievements and Industry Accolades
How we work
Our Quality Engineering Methodology
Discovery & Risk Assessment
Identify the type of AI system (LLM, ML model, agent, RAG), map the relevant regulatory requirements (EU AI Act, GDPR, HIPAA), and set success criteria and acceptable risk thresholds.
Data & Model Evaluation
Audit training data for quality and bias, benchmark the model against baselines, and run statistical validation (accuracy, precision, recall, F1).
Functional & Safety Testing
Hallucination detection, adversarial/red-team testing, fairness and bias testing across protected attributes, and safety-alignment/content-moderation checks.
Integration & Performance Testing
API and multi-system integration, latency/throughput/cost optimization, and scalability testing under production-like load.
Explainability & Compliance
XAI implementation (SHAP, LIME), audit-ready documentation, and EU AI Act / GDPR / HIPAA compliance validation.
Production Monitoring & Drift Detection
Continuous monitoring, input-anomaly and data-quality validation, behavioral drift alerts, and automated rollback triggers.
Continuous Improvement
Feedback loops built from production incidents, model-retraining validation, and ongoing compliance and governance reporting.
Measurable outcomes
Business Benefits
| Benefit | Impact |
|---|---|
| Faster regression cycles | Up to 80% reduction in test execution time |
| Less manual effort | 60โ70% reduction through intelligent automation |
| Fewer production defects | 65โ75% reduction in post-release issues |
| Lower QA costs | 40โ50% 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 | 70% fewer hallucinations. Up to 98% accuracy achievable. EU AI Act ready |
Our Tools & Frameworks
Web
Selenium, Playwright, Cypress, Puppeteer
Mobile
Appium, Detox, XCUITest, Espresso
API / Performance
REST Assured, Postman, Karate, JMeter, Gatling, k6
Visual / CI / Management
Applitools, Percy, Azure DevOps, GitHub Actions, Jenkins, TestRail, Zephyr
LLM Evaluation
Promptfoo, Ragas, TruLens, Azure AI Studio, OpenAI Evals
Bias / XAI
AI Fairness 360, What-If Tool, Fairlearn, SHAP, LIME, InterpretML
Adversarial / Monitoring
Garak, Rebuff, Lakera Guard, Arize, WhyLabs, Evidently, Fiddler
AI Test Automation
Testim, Mabl, Functionize, AccelQ, Copilot, LangChain, LlamaIndex
Cloud
AWS (EC2, Lambda, Device Farm, CloudWatch), Azure (DevOps, Test Plans, App Insights), GCP (Cloud Build, Test Lab, Monitoring)
Industry Expertise
Industries we serve
Healthcare
HIPAA, clinical decision-support AI validation, EHR/EMR integration, medical device software
Banking & Finance
Core banking, payment gateways, fraud-detection AI, model risk (SR 11-7), credit-scoring fairness
Insurance
Policy admin, claims automation, actuarial model validation, Solvency II / IFRS 17
Manufacturing
ERP/MES, IoT device testing, supply-chain AI, predictive maintenance
Retail & E-Commerce
Checkout flows, recommendation engines, inventory systems, personalization AI
Logistics
Route optimization, fleet management, warehouse automation, demand-forecasting AI
Automotive
Infotainment, ADAS software, connected vehicles, autonomous-driving simulation
Education
LMS, SIS, adaptive-learning AI, accessibility (WCAG, 508)
Telecommunications
Billing, network management, 5G service validation, CX AI
SaaS Platforms
Multi-tenant, API-first, microservices, AI feature integration
Public Sector
EU AI Act compliance, accessibility, transparency, high-risk AI validation
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
Bugs are detected from an early stage, and feedback is continuous.
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 โ 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 3โ5 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 donโt 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 โ 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.