Systems | Development | Analytics | API | Testing

Customers don’t buy software because they feel loved. They buy it because the product works, solves a real problem, meets security, scalability, and reliability requirements, and fits their budget. No amount of empathy or friendliness can compensate for missing features or poor performance. So at first glance, it’s easy to assume that great products alone win customer loyalty. But once the contract is signed and the product is in use, the rules change.

Over the past few weeks, an unusual experiment has been unfolding quietly and then very publicly.

The original promise of AI was that it would write most of the code for us. In reality, we’re not there yet. So where can AI meaningfully improve developer productivity today? In this post, we look at how AI powers development productivity across the SDLC, practical tools to use and frameworks for overcoming AI operationalization bottlenecks.

The preview of global queries in BigQuery lets you query data stored in different geographic locations with a single, standard SQL query, with no ETL.

If you're already using an analytics tool today, here are seven questions that will reveal whether it’s still the right one, not just for insight, but for risk control and long-term scalability.

In the age of AI, where delivery continues to accelerate, release confidence shouldn’t lag behind. Today’s software changes continuously, often generated or modified by AI. That raises complexity while shrinking the time quality teams have to plan, test, and make decisions. Manual workflows and static automation weren’t built for this pace.

AI-assisted software development makes building new features to help our customers an exercise in speed. Rainforest has a deep culture of experimentation and iteration, and we’re actively exploring how AI can help us work smarter. At our core, we aren’t just building an AI test generation tool; we are constantly experimenting with how AI can make our own development cycles leaner, faster, and more intuitive. That includes experimenting with AI-assisted software development.

In 2026, PropTech is no longer experimental. For most owners, operators, and investors, technology isn’t something you pilot — it’s core infrastructure. The shift is visible in both numbers and behavior. The global PropTech market is expected to grow from ~$34.4B in 2025 to ~$40.4B in 2026, with a projected ~17% CAGR through 2035. But growth alone doesn’t explain what’s happening.