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AI Testing Service
Fully Managed QA for AI-Powered Apps
Verify the reliability and compliance of your AI solutions
We tailor our QA methodology to your needs to address the unique challenges of testing AI-powered products to ensure quality, safety, and ethical standards for your innovative solutions.
AI Software Testing Services We Provide
We offer a comprehensive approach: a combination of traditional and AI-tailored testing.
Our team excels at validating AI-driven applications by addressing the unique challenges of NL, LLMs, and deep learning systems. We combine traditional QA practices with specialized AI-focused techniques to ensure models perform reliably and ethically in production.
Functional and Accuracy Testing
- Quality Assessment: analysing the accuracy, logic, and hallucination rate of responses on standard and challenging prompts.
- Behavioral Checks: verifying adherence to required output formats
- Adversarial testing: prompt fuzzing, format breakers, multilingual, long context.
Pre-Deployment Model and Data Validation
- Evaluations: creating automated tests on golden datasets to ensure a new model performs better than or equal to the one it's replacing.
- Training Data Audits: preventing data leakage and verifying dataset balance and correctness before training.
RAG Testing
- Retrieval Accuracy: measuring how precisely the system finds relevant information from its knowledge base.
- Attribution & Faithfulness: verifying that every response cites its source and remains free of hallucinations.
Bias, Fairness, and Ethics Audits
- Identifying Blind Spots: evaluating performance across demographic groups to uncover fairness gaps.
- Toxicity & Harm Testing: confirmation that even disguised harmful or offensive content is blocked effectively.
Basic Security and Vulnerability Testing
- Prompt-Based Attacks: checking resistance to jailbreaking, prompt injection, and data exposure attempts.
- Tool Security: ensuring integrated tools can’t be exploited to access internal systems.
Continuous Evaluation and A/B Testing
- Live Performance Comparison: measuring performance, user satisfaction, and cost across model versions.
- Safe Deployment: rolling out new models gradually (Canary/Shadow deployments) to minimise risk and enable rapid rollbacks.
Performance and Cost Governance
- Speed & Stability: testing response latency and system stability under high levels of concurrent requests.
- Latency benchmarking: throughput measurement, and SLA verification under realistic prompt sizes and tool latencies.
- Cost Control: validating token limits and cost controls, preventing unexpected and excessive API usage bills.
- Tracing & Observability: end-to-end observability validation for LLM execution flow control (prompt, retrieval, token use).
Production Monitoring and Observability
- Drift Detection: using built-in capabilities of observability platforms (e.g., Langfuse, Langsmyth, Prometheus), tracking distribution shifts in live inputs to prevent performance degradation.
- Automated Quality Assurance: collecting real-time metrics (accuracy, safety, latency, cost) from production traffic for proactive fixes.
Ensure reliable and predictable operation of your AI
Use our guide to see how to refine your testing approach to tackle AI-specific challenges and guarantee stable performance in real-world use.
Why choose QATestLab to Test AI Product
Tailored AI software testing process
QA methodologies will be adapted to your AI app’s unique logic, so you receive test coverage that is precise, reliable, and aligned with your product’s specific needs.
Ensuring ethical and secure AI behavior
Your AI product will be thoroughly validated in its critical behaviors before release, so it will be protected against reputational, legal, and ethical risks.
Fully managed skilled QA team
Through flexible, scalable delivery models, including fully managed teams, you will gain access to QA engineers with deep AI expertise, helping you maintain consistent quality across the AI testing process.
Comprehensive cross-environment validation
Your AI app will be tested on real devices, OS, browsers, and configurations, supported by our arsenal of 500+ physical devices, to confirm its performance and compatibility in real-world conditions.
AI-oriented testing aligned with industry requirements
QA processes will be aligned with industry regulations, so your product passes audits faster, meets market requirements, and is ready to scale in regulated environments.
Quick AI software testing Start
You get a response within 1 day and a quick project start within 1–3 days after signing the documents to ensure your product will be ready to launch within your desired timeframe.
Ensure your AI product is ready for real-world conditions
Fill out the form, and we'll get back to you to suggest an AI testing approach tailored to your goals and timelines.
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