QA AI Testing Services
Artificial intelligence (AI) is revolutionizing software testing by automating repetitive tasks, accelerating test cycles, and enabling deeper, more insightful analysis. Imagine AI generating test cases from user stories, automatically identifying visual regressions, and intelligently prioritizing tests based on risk. With SG Analytics (SGA’s) intelligent AI solutions, experience a future where testing is faster, smarter, and more reliable, with AI handling everything from data generation and test execution to defect prediction and root cause analysis.
Benefits of AI-Based Testing Services
Unlock a new era of quality assurance with AI-driven testing. We are moving beyond traditional methods by using AI to not only automate tedious tasks but also to provide predictive insights and vastly improve the efficiency and accuracy of your testing processes. Experience a future where software quality is elevated through intelligent automation.
AI Testing Solutions & Services
AI testing offers several distinct advantages over traditional testing methods, primarily by enhancing efficiency, accuracy, and adaptability. Whether it is automation of repetitive tasks, intelligent test case generation, or self-healing capabilities, our AI solutions provide a more proactive, efficient, and accurate approach to quality assurance.
Multiple Tool Support
SGA partners with multiple vendors to support AI tool support. We provide flexibility to customers to adapt AI processes and tools based on their specific requirements.
No Code/Low Code Automation
AI-powered test automation with no coding experience. Business users or manual testers with domain expertise can create test automation scenarios in plain, easy-to-understand English.
Use of GenAI in Software Testing
With mastered prompt engineering knowledge, SGA’s QA team uses generative artificial intelligence to generate test plans, test data, test cases, UI automation codes, API automation, framework code snippets, shift left testing, and database queries, among others, as per project requirements.
AI-Based Self-Healing Capabilities
Self-healing test automation can automatically detect, diagnose, and fix issues in applications without human intervention. This can help improve the efficiency of test automation and reduce the need for manual maintenance.
ML Models Testing
Expertise in functional testing of machine learning (ML) models. With extensive knowledge of AI and ML models, SGA provides testing solutions for supervised, unsupervised, and reinforcement AI models. We evaluate AI systems on various dimensions of responsible testing on biases and maintaining ethical standards and regulations.
Testing with AI Agents
AI agents are software programs that integrate AI to interact with their environment, collect data, and make decisions to perform tasks. They can work independently without human intervention. Our QA team is trained with refined tools that use AI agents for automation testing.
AI for Automation Testing
AI plays a significant role in automation testing, including aspects of code generation and issue resolution. Our AI-powered tools can assist in writing test scripts by suggesting code snippets or generating entire scripts based on user input.
Approach for QA/AI Testing Services
SGA presents a holistic and comprehensive approach to testing your AI-ML models, covering the entire AI lifecycle, from model validation to application testing. We focus on the importance of testing AI models for accuracy, completeness, and unbiasedness.
We begin with a detailed understanding of your AI system’s objectives and use cases to align with the goal and expectations.
With the understanding of expectations and objectives, a detailed strategy will be developed. Strategy emphasizes what to test, how to test, and when to test. Our testing plans and efforts will be aligned with the desired goals.
Validating the input data and testing data for accuracy, completeness, and bias.
The ML model will be checked for accuracy, performance, and generalization. The algorithm will be evaluated on factors such as the model not getting exposed to overfitting or underfitting with trained data, biases, unnecessary focus on one pocket, and other parameters.
We prioritize ethical AI testing. The fairness, transparency, and accountability of AI models are tested rigorously, making them adhere to regulations and ethical standards.
We simulate real-world conditions to check if the system responds as expected under varying workloads.
After deploying an ML model into production, multiple tests ensure its stability, performance, and accuracy. These tests validate the model’s real-world performance and monitor its behavior over time.
- AI testing/prompt testing
- DataOps
- MLOps
- Infrastructure and cost management
We begin with a detailed understanding of your AI system’s objectives and use cases to align with the goal and expectations.
With the understanding of expectations and objectives, a detailed strategy will be developed. Strategy emphasizes what to test, how to test, and when to test. Our testing plans and efforts will be aligned with the desired goals.
Validating the input data and testing data for accuracy, completeness, and bias.
The ML model will be checked for accuracy, performance, and generalization. The algorithm will be evaluated on factors such as the model not getting exposed to overfitting or underfitting with trained data, biases, unnecessary focus on one pocket, and other parameters.
We prioritize ethical AI testing. The fairness, transparency, and accountability of AI models are tested rigorously, making them adhere to regulations and ethical standards.
We simulate real-world conditions to check if the system responds as expected under varying workloads.
After deploying an ML model into production, multiple tests ensure its stability, performance, and accuracy. These tests validate the model’s real-world performance and monitor its behavior over time.
- AI testing/prompt testing
- DataOps
- MLOps
- Infrastructure and cost management
Why Choose SGA’s QA/AI Testing Services?
Our Ins(AI)ghts
Whitepaper
Leveraging AI and Machine Learning for Data Quality Management
As organizations embrace digital transformation, the volume and value of data are growing exponentially. At SG Analytics, we help enterprises elevate their data quality management (DQM) practices by leveraging artificial intelligence (AI) and machine learning (ML). Our solutions automate data cleansing, improve accuracy, and ensure consistency—enabling faster, more reliable decision-making. By embedding intelligence into DQM, businesses can reduce risk, optimize operations, and unlock the full potential of their data assets for sustained growth and competitive advantage.
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