From documents and semantic retrieval to workflows, multi-model AI, voice, APIs, and deployment: a developer's look at the architecture behind a modern AI-agent platform.There is a point in almost every AI project where the demo stops being interesting.The first version is usually easy to get excited about. You upload a few documents, connect a model, type a question, and suddenly you have a chatbot that appears to understand the business.Then reality arrives.The documents get bigger. Different teams need different knowledge bases. Users ask questions that are not in the original dataset. Conversations become longer. Someone wants the chatbot on a website. Someone else wants an API. Then comes voice, lead capture, analytics, permissions, model selection, and workflow logic.At that point, you are no longer just building a chatbot. You are building a small AI platform.That is what caught my attention when looking at AIUniverse. Its public product architecture is built around bringing bus...
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