The biggest challenge for enterprises in the rapidly evolving world of Generative AI isn’t just building “smarter” LLMs or agents — it’s securely connecting that AI to the decades of business logic and data locked away in enterprise systems. How do you let an AI agent interact with your Salesforce data, your Kafka topics, or your internal databases without rewriting everything or creating a massive security hole? It turns out the answer may already be running in your organization.
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AI
In the rapidly evolving landscape of AI-powered applications, the ability to process and understand documents has become increasingly crucial. Whether you’re dealing with PDFs, Word documents, or PowerPoint presentations, extracting meaningful insights from unstructured data is a challenge many developers face daily. In this post, we’ll explore how Apache Camel’s new AI components enable developers to build sophisticated RAG (Retrieval Augmented Generation) pipelines with minimal code. We’ll combine the power of Docling for document conversion with LangChain4j for AI orchestration, all orchestrated through Camel’s YAML DSL.
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AI
Introduction In this blog post, we’ll explore how Apache Camel JBang’s Infrastructure Command can help you rapidly prototype end-to-end integration scenarios and adapt to changing requirements. We’ll walk through a realistic development scenario where requirements evolve over time, demonstrating how Camel’s flexibility makes it an ideal choice for proof-of-concept development. Camel JBang Infrastructure: Your Prototyping Toolkit We already know and love Camel JBang (if you don’t, check out Claus Ibsen’s YouTube channel for excellent tutorials).
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