Systems | Development | Analytics | API | Testing

How does GDPR impact AI innovation, and what affects might AI have on regulations like GDPR? According to McKinsey, 78% of companies now use AI in at least one area of their business as of July 2024. But this quick adoption brings challenges for organisations handling data from the European Union and the UK. The main challenge for InfoSec and other enterprise leaders is clear. Using AI effectively means being able to develop faster.

The demand for AI-driven applications is surging, and enterprises have reached an inflection point where they can no longer afford fragmented, siloed development. Traditionally, AI development is done by data scientists or machine learning experts with deep expertise in multiple tools and frameworks.

AI is everywhere. Depending on who you ask, it’s either making developers obsolete, or it’s just hype. But for those of us who’ve actually used AI tools in real-world engineering workflows, especially in complex distributed systems, the truth lies somewhere in between. At Multiplayer, we’ve spent the past few years exploring how AI can—and can’t—help solve two of the most persistent challenges in distributed systems: documentation and debugging.

Modern companies generate large volumes of data, but often the internal users who need that data to do their jobs—data engineers, managers, business analysts, and developers—can find it challenging to quickly figure out answers to their questions. Apache Kafka is a powerhouse for real-time data processing of high-throughput workloads, and many organizations use Kafka to enable self-service access to data streams.

At ThoughtSpot, we’re on a mission to empower every business user to become a data champion. Over the past year, I’ve witnessed firsthand how organizations across Australia and New Zealand are embracing this vision, transforming the way they work, make decisions, and serve their customers. Today, I’m excited to share some of the incredible momentum we’re seeing in the region and to celebrate the forward-thinking organizations leading the charge.

Gen AI call center co-pilots can provide enterprises with operational visibility and insights while automating repetitive tasks, to improve the customer experience. In this session, we’ll show how a large health insurance provider implemented an agentic co-pilot designed scale across multiple call centers and environments. To dive deep into the architecture and see a demo of the co-pilot, you can watch the webinar this blog is based on.

You’re probably familiar with Android Geofencing already, but in case this is your first rodeo, it’s a location-based system that can receive access to a mobile device’s location data, and trigger all kinds of functions when users pass in and out of specific areas. The ads our users see, the content they can stream, the offers they receive via push notifications… all can be triggered by geofencing.

As cloud native applications grow, managing resource usage and controlling costs becomes increasingly complex. Choreo's Cost Optimizer addresses this challenge by providing AI-driven insights and actionable recommendations, enabling developers and organizations to streamline resource utilization and reduce unnecessary expenditures.