7 things engineering teams get wrong about AI-powered QA 
When engineering teams evaluate AI-powered QA tools, the same questions come up again and again. Some are rooted in genuine technical curiosity. Others stem from experiences with earlier-generation tools that earned a healthy dose of skepticism. After hundreds of these conversations, I’ve identified the seven most common misconceptions.
Rainforest vs. QA.tech: How the tools actually compare 
If you want to move fast and feel confident in your releases, the way a QA tool applies AI matters more than the fact that it uses AI. Compare Rainforest QA and QA.tech.
From meeting transcript to production-ready code in 40 minutes: Building the future of AI testing 
Rainforest QA has a deep culture of experimentation and iteration. Recently, we decided to test out taking an idea from meeting transcript to production code. It took us 40 minutes. Here's how we did it.
5 Lessons learned building a web application crawler 
How we built a custom web crawler to solve QA test planning. Learn about handling authentication, SPAs, interactive elements, and the unexpected challenges of crawling modern web applications.
Watch: Using generative AI for test automation in Rainforest [Video]
See how Rainforest uses generative AI for automated test creation and maintenance.
Surprise, your data warehouse can RAG 
How to use your data warehouse's built-in features to simplify and potentially improve your RAG pipeline.
Building reliable systems out of unreliable agents
This is the process our engineering team uses to create reliable AI systems out of unreliable AI agents.