Systems | Development | Analytics | API | Testing

AI failures rarely look like crashes. More often, they appear as confident but incorrect answers, subtle bias, or culturally inappropriate responses.

Everyone thinks AI is getting cheaper. But is it really? At first glance, the economics of AI seem to be improving for everyone. Thanks to continued model optimization and advances in hardware, the cost of running LLMs (also known as inference) is steadily decreasing. Developers today can access incredibly powerful models at a fraction of what it cost just a year ago. But there’s a catch.

If your BI and analytics tool isn’t powered by Agentic RAG AI, you’re missing out on advanced AI capabilities that enhance efficiency and visibility into data. Whereas agentic AI can work autonomously and without human intervention, RAGI AI combines the best information retrieval methods available with the power of AI. The result is deep knowledge of organizational data that improves over time.

Distributed rate limiting is essential for managing traffic across multiple servers, ensuring fairness, preventing abuse, and maintaining system reliability. Unlike local rate limiting, which works on a single server, distributed rate limiting uses a centralized datastore to enforce limits globally, making it ideal for large-scale applications and multi-node setups.

Is your organization still managing contracts the traditional way, spending weeks drafting, reviewing, and negotiating terms? Are manual errors, compliance risks, and value leakages quietly affecting your bottom line? Well, in today’s competitive business environment, where every decision is under the microscope, inefficient contract management is a strategic vulnerability. And that’s where Gen AI comes into play.

The advancement and widespread availability of new artificial intelligence (AI) capabilities—through platforms like the Databricks Data Intelligence Platform and Mosaic AI—has completely reset expectations for engineering teams across every industry. Business now moves at a new pace, demanding rapid delivery of intelligent, real-time applications—instead of slowly stitched-together systems solving problems defined and scoped months prior.