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I work in marketing, but I spend more time in automation flows than in campaign briefs. And I love it. ‍ At Keboola, I’m part of the Marketing team, but behind the scenes, I’m the one wiring up lead capture, validation, routing, enrichment, and notifications. Every form submission, every hot lead, every triggered campaign has to land somewhere fast and clean. ‍ That’s why I’m genuinely excited about this: Keboola and Make have officially partnered!

Remember when employees went rogue with cloud apps and Shadow IT became IT's nightmare? Well, meet its chaotic sibling: Shadow AI. Employees everywhere are quietly dabbling with AI tools, and IT usually has no idea. It's fast, it's convenient, and it's also a ticking security bomb. Nearly 80% of companies already faced some AI-driven fiasco, from data leaks to embarrassing decisions - and IT leaders are seriously freaking out (TechRadar, 2025). But banning AI isn’t the answer.

Sometimes the biggest opportunities come disguised as unproven protocols released on a random Monday. Here’s why we bet on MCP before anyone asked us to. ‍ Two months before anyone knew what MCP was, we made a bet that it would fundamentally transform how people interact with their data infrastructure.

‍After 10 days since launching the Keboola MCP (Model Context Protocol) Server, we've gathered the most common questions from our data community. This article combines practical answers with best practices inspired by successful AI-assisted development patterns, helping you get the most out of your AI-powered data workflows.

Keboola Data Templates are AI‑enhanced, reusable pipelines you launch with one click or API call. They eliminate the need to rebuild data workflows from scratch—so every use case is a fast-track to insights and impact. No more reinventing the wheel: once a template exists, it can be shared and adapted across departments—marketing, finance, ops—saving weeks of build time and hundreds of engineer-hours.

The biggest issue with chatbot implementations powered by generative AI is the accuracy and reliability of the output. Models can give erroneous or inaccurate answers due to hallucinations or simply because they lack information specific to a given business case, as many of them don’t have access to new data outside of pretraining. Retrieval-Augmented Generation (RAG) is a technique designed to address this limitation by integrating an external retrieval mechanism with a generative model.

The global financial services industry is a complex landscape: a patchwork quilt of regulatory frameworks, technologies, and markets with very different customer needs. To be the most profitable, the most competitive, and the most efficient it can be, a financial services provider must find a way to navigate that complexity, and sales and finance teams hold pieces of the puzzle. And putting the pieces together isn’t as easy as it may look at first glance.

The e-commerce landscape is passionately competitive. Today is a prime example as consumers rush to secure the perfect Valentine’s Day gift before it's slim pickings on the chocolates and the sad looking roses! How do you meet the demand and keep those last minute bouquet hunters happy?

We at Keboola believe that there can’t be too much efficiency in data engineering. Any data professional would surely agree, there is no such thing as good enough. And while better tools sometimes offer incremental steps, pushing the needle a little step at a time, a combination of tools can lead to impressive synergies. Keboola CLI and Cursor AI are exactly that—a combination that’s more than a sum of its parts.

Environmental, Social, and Governance (ESG) reporting is no longer a forward-thinking corporate initiative—it’s an immediate business imperative. With regulatory deadlines fast approaching, stakeholders demanding transparency, and the financial risks of inaction rising, companies must move beyond reactive reporting to proactive ESG data management.