Systems | Development | Analytics | API | Testing

While every organization races to deploy AI agents faster, finance departments are watching something alarming unfold—and it will play a large part in determining who survives the agentic era. The numbers are stark: 84% of companies report more than 6% gross margin erosion from AI costs. Within that, 26% report erosion of 16% or more. And only 15% of companies can forecast AI costs within ±10% accuracy—the majority miss by 11-25%, and nearly one in four miss by more than 50%.

Choosing the right AI test case builder requires evaluating integration depth, not just feature lists. Evaluate AI test case builders based on how they enhance your current workflow rather than how many features they advertise. Your QA team is drowning in test cases. Requirements change daily, releases accelerate weekly, and manual test creation has become the bottleneck everyone acknowledges but nobody has time to fix. An AI test case builder seems like the obvious solution.

Artificial Intelligence in real estate is no longer a future concept or a conference buzzword. It’s already reshaping how properties are leased, managed, valued, and invested in — often quietly, behind the scenes, inside operational workflows. Over the past months, ORIL has been hosting conversations with founders, CEOs, operators, and technology leaders on the Innovation Blueprint podcast, discussing how AI is actually being used in PropTech today. Not hypotheticals. Not hype.

You have more data than ever, but getting a simple answer feels impossible. Your data lives in dashboards you can’t question and reports that are outdated the moment they’re published. You’re paying for analytics tools that most of your team never touches. And when you actually need an answer – in a meeting, on a call, right now – you’re told to wait for someone to pull a report.

So as software engineers using AI coding assistants, we’re quickly learning of a new anti-pattern: Hallucinated Success. You give your agent (e.g. Claude via terminal or various IDE code assistants) the command “refactor the billing controller.” The agent happily complies, churning out nice clean code. The agent even goes so far as to write a new unit test suite that passes at 100%. You integrate it. Your test suites pass. Your production code breaks. Why?

Learn what a data lakehouse is, its core architecture, and how it compares to data lakes and data warehouses to solve modern data challenges.

Key Takeaways As companies continue to scale analytics beyond operational systems, transferring data from generic databases (like MongoDB) to cloud warehouses (like Snowflake) becomes critical.

Have you ever noticed how your favorite apps just know what you want? Whether it’s a curated playlist that suits your mood, a movie recommendation that hits the spot, or ads that seem oddly relevant, none of it feels surprising anymore. These experiences have become so routine that we barely pause to think, “How does this even work?” But maybe we should.

Complex user tasks often need multiple AI agents working together, not just a single assistant. That’s what agent collaboration enables. Each agent has its own specialism - planning, fetching, checking, summarising - and they work in tandem to get the job done. The experience feels intelligent and joined-up, not monolithic or linear. But making that work means more than prompt chaining or orchestration logic.

From shopping and food delivery to banking and fitness, mobile users everywhere expect smooth, fast, and bug-free experiences. Behind every efficient mobile app is a team of testers working hard to make that happen – and if you’re one of them, you know it’s no easy task. Mobile testing isn’t just about checking whether a few buttons work.