Redefining Quality at Speed with AI-Led Assurance
As digital transformation accelerates, traditional testing methods struggle to keep up with the velocity, complexity, and volume of modern software delivery. At ACL Digital, our AI-led Quality Engineering Services help enterprises confidently navigate this shift—redefining QE as more than a checkpoint. We embed AI across the QE lifecycle to drive greater speed, accuracy, and intelligent decision-making.
By combining an automation-first approach, domain-led frameworks, and scalable quality infrastructure, we transform quality engineering into a strategic enabler of innovation and trust. Our services are designed not only to enhance test productivity and accuracy but also to ensure that your AI systems align with principles of Responsible AI, including governance, fairness, and explainability. The result is faster time-to-market, reduced costs, and seamless, high-quality digital experiences.
15+
Years of QA Expertise
500+
Successful Projects Delivered
90%
Manual Effort Reduced with AI Automation
Global
QE Delivery & Agile Engagement Pods
Advanced QE Solutions Backed by Deep Expertise
Proven QA Legacy + AI Innovation
15+ years of QA excellence now supercharged with AI tools, techniques, and domain-first frameworks.
Smarter Testing, Powered by AI
AI-powered automated test plans, functional test cases, functional test scripts, and automated API test generation—saving up to 90% of manual effort and enabling faster execution with better coverage.
Innovative Test Automation Strategies
End-to-end coverage from ideation to post-deployment. Our AI-first QE services span across web, mobile, APIs, cloud, containers, and AI-led products.
Transformative Testing Services
Flexible engagement models including global delivery, agile pods, and on-demand accelerators to match your scale and pace.
AI-Led Quality Engineering Consulting for Next-Gen Intelligent Systems
Our Pillars of Responsible AI
Governance & Compliance
We ensure adherence to global standards like GDPR with audit trails, PII masking, and quality checks.
Ethical & Secure Validation
We test models for bias and fairness, simulating attacks to proactively uncover and fix vulnerabilities.
Observability & Explainability
Observability & Explainability We track decision logic, detect hallucinations, and monitor drift using interpretability tools.
Ongoing Monitoring & Reporting
We deliver live dashboards and regular reports for clear visibility into risks, coverage, and compliance.
Why Choose ACL Digital for AI-Led Quality Engineering?
ACL Digital’s AI-led Quality Engineering services are designed to ensure high-speed, intelligent, and secure software delivery. Our future-ready QE approach combines proven QA expertise with cutting-edge AI capabilities to maximize coverage, reduce costs, and accelerate deployment.
Up to 60% Reduction in Test Automation Effort
Achieve faster execution and streamlined testing processes by automating repetitive tasks with AI.
30–40% Cost Savings Through Optimization
Lower QA spend with intelligent resource allocation and test suite optimization.
Higher Code & Test Coverage
Enhance product reliability with broader and deeper testing backed by AI-driven insights.
Accelerated Deployments with Precision
Improve release velocity through smart automation and continuous validation.
Seamless Third-Party Integrations
Ensure compatibility and extensibility with out-of-the-box integration support.
Enhanced Test Efficiency
Prioritize intelligently to focus on high-impact areas and reduce testing cycles.
End-to-End Test Automation
Drive continuous assurance across development, staging, and production environments.
Always-On Security Assessments
Safeguard applications with persistent security validation across application and infrastructure layers.
Industries We Serve
Our deep understanding of industry-specific challenges allows us to deliver tailored QE solutions to:
Startups
in the ideation or MVP phase
Mid-sized firms
aiming to scale operations and improve release velocity
Large enterprises
seeking to modernize QA, integrate AI, and ensure robust, secure software systems
We work across verticals including BFSI, healthcare, retail, manufacturing, telecom, and high-tech—ensuring reliability and resilience in every product we test.
Our Methodology for Validating AI Systems
As organizations adopt AI at scale, ensuring the safety, reliability, and fairness of intelligent systems becomes critical. Our methodology combines proven quality engineering (QE) practices with AI-specific validation techniques to help clients build trustworthy AI solutions.
Wide Test Coverage
Comprehensive testing across large language models (LLMs) and machine learning (ML) systems to identify unsafe, unintended, or biased behavior.
Manual and Automated Testing
Blending hands-on evaluation with automated tools to validate functional accuracy, ethical considerations, and adversarial robustness.
Edge and Adversarial Case Validation
Rigorous red teaming and simulated attack pipelines to uncover vulnerabilities and ensure model resilience under stress conditions.
Functional and Performance Testing
Verifying that AI models meet their intended use cases, perform reliably across environments, and deliver consistent outcomes.
Our Accelerators and Frameworks
We bring innovation to testing through proprietary frameworks and AI-powered accelerators that cut down manual effort, boost speed, and ensure intelligent coverage.
ToxiGen, RealToxicityPrompts for toxicity detection; BBQ for bias evaluation; SQuAD, Natural Questions, ARC, TruthfulQA, and HellaSwag for measuring factual accuracy, reasoning, and logical consistency. LLM-as-a-Judge methodology to evaluate chatbot responses for quality, coherence, and correctness.
NVIDIA NeMo Guardrails, Guardrails AI (guardrailsai.com), and LangKit (Lightricks) for enforcing responsible AI practices and building robust guardrails. RAGAS for evaluating Retrieval-Augmented Generation (RAG) applications.
IBM AI Fairness 360, Fairlearn (Microsoft) for bias and fairness checks. SHAP and LIME for Explainable AI (XAI) insights.
Jira, TFS, Redmine, Bugzilla, Mantis Bug Tracker, Test Link, Rational Software, and HP Quality Centre.
xUnit, Wire Shark, Fiddler, TCPDUMP, Postmark, Blazemeter, Burp Suite, Iometer, OWASP ZAP, and NMAP.
Selenium, Appium, Apache JMeter, Visual Studio, Robotium, Postman, Android UI Automator, QF-Test, TestComplete, Calabash Testing Tool, HP Loadrunner, Espresso, Cucumber, SoapUI, Cypress, Tricentis Tosca, Playwright, Karate.
Jmeter, NeoLoad, Loadrunner.
Our Tools and Technologies for Enhanced Product Quality and Reliability
Client Impact
Accelerate Display Device Testing with Automated Solutions
We implemented a comprehensive testing automation solution, VOLANIUM, to optimize testing cycles and ensure product quality for a US-based manufacturer.
The Challenges
- Over 4000 test cases on Android devicesfrom the sensors
- Inability to perform parallel testing across multiple devices
The Outcomes
- Achieved 85% test automation coverage, significantly boosting team productivityg.
- Reduced release testing time from 45 days to just 1.5 days.
- Ensured product quality and reliability through rigorous testing.
Complete Automotive Wheel Management System Testing with ACL Digital
We provided comprehensive testing services, including lab and field trials, to ensure the quality and performance of a complex wheel management system for a US-based technology company.
The Challenges
- Over 1500+ test cases required for comprehensive validation.
- Absence of a robust multi-stage testing approach.
- Lack of functional and non-functional testing coverage.
The Outcomes
- Achieved a high level of test effectiveness, ensuring product quality and reliability.
- Optimized testing efforts through efficient lab and field testing.
- Accelerated time-to-market with streamlined testing processes.
- Mitigated risks and ensured regulatory compliance through rigorous testing.
Robust Penetration Testing for Industrial IoT Gateways to Fortify Security
ACL Digital conducted comprehensive penetration testing to identify vulnerabilities, strengthen IoT gateway security, and ensure robust compliance for seamless industrial IoT operations.
The Challenges
- Identifying security vulnerabilities in IoT gateways
- Ensuring compliance with industry security standards
- Mitigating risks of unauthorized access and data breaches
The Outcomes
- Detected and resolved critical vulnerabilities in the IoT system
- Enhanced gateway security with improved threat resilience
- Achieved compliance with key industry standards
- Ensured secure industrial IoT operations at scale
