Systems | Development | Analytics | API | Testing

Generative AI models are not just tools for producing text, audio or video—they're systems that learn patterns, improvise, and generate unexpected outcomes. When we look at LLMs, we're struck by their capacity to generate surprisingly creative and context-aware results. They can weave coherent narratives, propose novel solutions, mimic human conversation, and even offer nuanced insights across a wide range of topics. While this creativity is their strength, it also introduces variability and risk.

At the top of many IT folks’ minds lies the looming 2027 deadline for SAP’s end-of-life for SAP ECC – and the subsequent migration to S/4HANA. Often a complex and lengthy migration process, the shift to S/4 requires careful planning and resource allocation.

Testing AI-powered applications is one of the top priorities for teams deploying machine learning systems at scale. As AI becomes embedded in everyday tools, its behavior becomes harder to control.

We're excited to introduce a major update to the Choreo CLI: support for the Model Context Protocol (MCP). This enhancement brings conversational AI capabilities into your development workflow, enabling you to manage your Choreo environment using natural language commands.

Want secure, AI-ready data access? DreamFactory's Role-Based Access Control (RBAC) system simplifies managing who can access what in your databases and APIs. Why it matters: Poor data management causes 1 in 3 AI projects to fail. Breaches by insiders cost $4.99 million on average, yet only 24% of AI projects include proper security. How DreamFactory helps: Assign roles to users, control access to specific data, and limit actions (like view vs. edit) without custom code.

For data analysts and engineers, the journey from raw data to actionable business insights for business users is never as simple as it sounds. The semantic layer is a critical component in this process, serving as the bridge between complex data sources and the business logic required for informed decision-making. However, not all semantic layers are created equal, and the evolving landscape of AI-powered analytics demands a new approach.

Agriculture has long been the backbone of human survival, proof of our deep connection with and dependence on nature. But as the world evolves, so do the challenges in farming. From shrinking arable land to unpredictable weather patterns, how can farmers keep up? How do we ensure there's enough food for future generations? There’s no doubt that farming has come a long way, but let’s be honest, it’s getting harder every year.

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.

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.

When you think of artificial intelligence in transportation, what’s the first image that comes to mind? Is it self-driving cars smoothly cruising city streets, or maybe delivery drones zipping through the skies? While the dream of fully autonomous vehicles might still be on the horizon, the reality is that AI is already reshaping how we move, every single day. Look how our Co-founder is enjoying the view in a self-driving car in the USA! ‍ Yes, you saw that right, it’s fully autonomous.