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…How? Does the and then how to think about calling them. The third question you asked is, you know, we have a link that I'm happy to give you on supervised generation. It's an inference endpoint and we are going to release the, you know, kind of the overall structure and it really answers that question algorithmically. So it searches at the beginning, but the other thing it does is it forks off searches as the model is writing. So it will discover, let's say we asked it something generic about open AI and ChatGPT and then it all of a sudden discovers, so a new model dropped yesterday that's like a new topic and it actually forks another search to bring in that new topic into the next paragraph. So instead of search at the beginning and large language model takes over, we really think it should be like working in concert to fill out a fuller dossier of new things that it discovers that probably weren't in the initial search that are just actually things that you learn from the search result coming back. And so that winds up the supervised generation generally does in that loop somewhere between 12 and and 35 searches, which really means that that whole experience that is a lot more a fulsome answer still is 1/3 the cost of like one brave search. And then the tokens on the other side are about the same no matter what model you use. So we just think it opens up for new experiences. AvePoint is building the control layer for AI agents so you can securely govern, audit, and recover every action at scale. Design trusted agentic outcomes from day one at https://avpt.co/tcr Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr So at gtci think you you revealed that you're using the Nematron 3 Nano Nvidia's LLM model. I think it's very, it's a very fast small model. And is that is that the model that is being used to kind of do the supervised generation, kind of an iterative process of search where you search from your index and then after that whatever is found, then it's processed by the Nematron 3 nano and then a new set of queries are created and then that that continues the query process.…

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