One of our clients, an international IoT security and automation company, faced this challenge when automation expanded from web to iOS and Android products. To support the growing scope, we introduced a fully managed AI-augmented testing approach, using AI within the automation workflow under continuous engineers’ supervision.
We recently worked on a case: an AI learning platform available through a desktop app and a browser extension. It helps employees learn to use tools like ChatGPT and Copilot in real workflows. Our goal was to make sure the product interacted reliably with integrated AI services, including its behavior and response consistency, while also ensuring a clear and convenient experience as users interact with the AI tools they are learning to use.
A software company needed to validate 13 AI-driven editing features on Windows and macOS. With a tight 3-day release window and an insufficient in-house device pool, they had to check software compatibility across various RAM and GPU setups quickly.
This case study shows how structured QA turned an underperforming AI tutor into a retention driver. A US-based EdTech company launched a personalized AI assistant to guide learners through self-paced courses. Within three days, 58% of users stopped using it.
Unreliable AI risks patient safety and product adoption. We turned our hands-on testing approach into a practical guide that will help you verify that your model is safe for real-world healthcare appliances. This checklist outlines the quality framework for building dependable, high-performing AI agents.
Unstable AI risks user trust and company reputation. If your AI product hallucinates or breaks integrations, you lose customers. We proved this with a client who increased capitalization by over 50% by improving their quality process. This mini-guide outlines the framework for turning a fragile MVP into a stable, mature platform.
To help you check whether your AI test strategy is responsible, we’ve prepared a short Yes/No self-assessment checklist. It helps identify gaps in your current testing strategy and highlights what typically happens when critical validation steps are skipped.
Operational failures of an AI agent in production can lead to data loss, poor user experience, and security risks. These issues directly affect user trust, product adoption, and overall business success. That’s why it is critical to ensure the AI agent operates correctly before it interacts with real users. Rigorous pre-release testing helps to identify weaknesses early and confirm consistent, reliable behavior in real-world conditions.
AI features like chatbots, recommendation engines, and autonomous agents bring new risks that traditional QA often misses. This guide outlines how to adapt your testing process to handle AI-specific challenges and ensure stability in real use.
AI agents operate with a level of autonomy that introduces unique testing challenges — from long-term memory consistency to goal-based behavior and ethical reliability. Our presentation outlines how we test AI agents — autonomous systems designed to plan, decide, and respond over time.
