Systems | Development | Analytics | API | Testing

Which AI Analyst Holds Up Best for Your Hard Questions?

Analytics vendors claim their AI answers questions accurately, but almost none of them will show you how they checked. The standard move is a percentage with no denominator: "90%+ accuracy on internal benchmarks." No dataset you can download. No scoring method you can inspect. No competitor runs under the same conditions. You're asked to trust the grade without ever seeing the exam1 We ran the exam in public terms instead.

Start your AI agent testing with deterministic tooling

By now everyone is aware of the limitations inherent in generative AI and the AI agents that use it to complete their tasks, and the challenges involved in getting them enterprise quality. If you are planning to incorporate AI agents into your enterprise IT architecture, how are you planning to validate their quality and accuracy?

AI's Impact on Automated Test Script Generation

AI-powered automated test script generation is transforming how software teams approach quality assurance. By analyzing real user behavior, code changes, and system logs, these tools reduce the time and effort needed to create and maintain tests. This shift from manual scripting to AI-generated scripts helps teams keep test coverage in sync with rapid release cycles.

iOS 27 Is Here: How Perfecto Keeps Your Apps Release-Ready

Apple's iOS 27 release marks another significant shift in the mobile landscape. Beyond new features for iPhone users, it signals a familiar pressure point for QA and development teams: the clock starts ticking the moment the update goes live, and your apps need to be ready. Here's why that matters: nearly 80% of iPhone users adopt the newest iOS version almost immediately. That high adoption rate leaves little room for error. If your app breaks on the iOS 27 release, your customers notice fast.

Automating Trust: How Business Review Management Fits Into the Modern Ops Stack

Most companies still treat reviews as a marketing chore. Someone on the team remembers, sends a batch of emails, watches a few stars roll in, and forgets about it for six weeks. That model is finished. Consumers now expect a reply within days, they discount anything written more than three months ago, and a growing share of them never read your reviews at all because an AI assistant read them first and summarized the themes. None of that can be served by a human remembering to check a dashboard.

Bring your test data into the AI tools you use | SmartBear MCP Server for Zephyr

Not everyone who needs a read on release readiness lives in Jira. This video demonstrates how the SmartBear MCP Server brings trusted Zephyr test data into Claude, so anyone who needs access to testing data gets it without switching context. A QE lead checking coverage, a release manager confirming a ship date, a developer following up on a fix, and any stakeholder who just wants a fast answer has what they need.