Learn how a natively multimodal database like ApertureDB helps healthcare ads stay compliant by flagging missing facts and improving transparency by supporting true multimodality alongside vector search. Longer abstract: Technologies like RAG (retrieval-augmented generation), semantic search systems, and generative applications wouldn’t be possible without vector databases. A very few of these databases, such as ApertureDB, are truly capable of natively handling more than just text. They now work with images, audio, and other data types, which opens up new possibilities across industries like healthcare, retail, and finance. For building this example, we pick healthcare advertising because it shows a great blend of multimodality. With strict rules around accuracy, disclosure, and patient privacy, it’s critical to include all Material Facts in marketing content. These are details that could influence a patient’s understanding or choices. In this blog, we will discuss how a combination of ApertureDB, Unstructured, and OpenAI can help detect and flag missing material facts in healthcare advertisements.

What if the computer itself can think and take actions for you? You just give it a goal, and it performs every click, type, drag, and gets work done across the desktop and web. In this talk, Zengyi reveals the breakthrough technology that his company OpenAGI is developing: AI that can use computers like humans do. He talks about how his team developed the model, why it outperforms similar models from OpenAI and Google, and its wide use cases across different domains.

Feature stores might be the wrong abstraction. Varant Zanoyan and Nikhil Simha Raprolu explain why Cronon ditched “store-first” thinking and focused on compute, orchestration, and real-time correctness—born at Airbnb, battle-tested with Stripe. If embeddings, agents, and real-time ML feel painful, this episode explains why.