Systems | Development | Analytics | API | Testing

An AI Data Gateway is a secure intermediary that connects enterprise data sources (like databases and file systems) with AI systems. It simplifies how AI accesses data while enforcing strict security, compliance, and governance measures. Instead of allowing direct access to sensitive data, the gateway uses secure REST APIs to control and monitor all interactions.

AI connectivity is enterprise infrastructure that governs, secures, routes, observes, and optimizes all AI interactions—providing the control layer organizations need to transform experimental AI into production-ready systems.

Data Apps are interactive web applications that run directly in your Keboola project. They let you visualize, explore, and interact with your data without needing external BI tools. Think of Data Apps as your custom dashboards, built exactly how you need them. Now, let's see how Kai makes building Data Apps effortless.

BigQuery Studio’s latest Gemini-powered assistant goes from being a code assistant into a fully context-aware analytics partner.

The data visualization landscape is experiencing a seismic shift. No longer is it enough to simply present dashboards filled with colorful charts and metrics. Today's decision-makers need something more powerful: the ability to understand what their data actually means, why trends are occurring, and what actions to take next.

According to Perforce's 2026 State of DevOps report, there is a direct correlation between DevOps maturity and AI success. In a highly mature DevOps environment, AI accelerates innovation, optimizes workflows, and enhances security. In an immature environment, it scales chaos, multiplies risk, and inflates costs. So, before we ask ourselves how to make the most of our AI solutions, we must assess if our foundational processes are prepared for the challenge ahead.

Are you still evaluating when and how to implement AI across your asset and wealth management operations? While many organizations remain in the planning stage, others have already started integrating AI into their decision-making frameworks because AI adoption in the FinTech space has matured enough. According to the PwC Asset & Wealth Management Report, firms adopting AI-led transformation could see up to a 12% revenue increase by 2028.

Your AI chatbot might give a customer the wrong price. A RAG-based support agent might cite a document that doesn’t exist. An AI coding assistant might suggest code with a security problem. These issues are common for teams releasing LLM features without proper testing. The reality is that many teams using GPT, Claude, or Gemini don’t have a strong testing strategy. They usually do a few manual checks or simple prompt tests and assume it’s enough.