With GPT 5.6 saw, we've seen huge improvements in the build-out of enterprise workflows within v7go. A few months ago, the models couldn't really understand complex processes like insurance underwriting or screening a finance deal, but the latest GPT models can ingest 100 pages of instructions and build out a workflow that can solve them end-to-end. v7go helps GPT models develop these long-horizon workflows with citations that anchor every fact, and more recently, a memory system that remembers everything about your company. We benchmark every new model, and with GPT 5.6 solved, we noticed that tool call errors in our context graph benchmark dropped from 2.7% to just 0.2%. Yeah, but the model is only half the equation, because the other half is the platform where these models run on. And our context graphs store all this information about entities, facts, relationships, following an ontology that matches your data. Previously, models used to make ingestion errors, especially whether a fact was worth remembering or not. Older models using RAG would also try and guess unanswerable queries. With GPT Luna on v7go context graph, we've seen hallucinations reduce by 38%. Visual understanding of documents is also getting much better. And we're seeing the same benefits in workflows as well, where previously we took minutes to research companies' information and then just give the user an output. Now we take just a few seconds and we don't need to re-ingest all the data from scratch. We're also seeing the quality of the output improve. So previous models used to generate decent investment memos, but they lack the depth and accuracy that we're seeing with the new models. Yeah, so the GPT 5.6 suite is letting us solve use cases that a year ago were impractical or even impossible. And combining that within a workflow engine and a memory system, we can help these enterprises that are known for their slowness in retooling towards the HOAI. And we're thrilled to be working with OpenAI towards that.