Extraction models turn human language into data your software can act on. We're bringing them to Sage. Early access tomorrow for everyone who registers on @levantolabs by PT today. Demo 📺👇 Most software needs structured inputs. People communicate through emails, chats, tickets, contracts, and conversations. Extraction models connect those two interfaces. You define the information your application needs, and the model identifies it in the text: people, companies, products, dates, amounts, descriptions, or requests. Calling this “extracting stuff from documents” understates what developers can build with it. Consider a customer message: “The blue jacket arrived with a broken zip. Please send a replacement to 18 King Street.” An extraction model can identify the product, defect, requested remedy, and address. Your application now has fields it can validate, look up, and pass to a tool. A decision model can then evaluate whether the request qualifies for an automatic replacement, needs more information, or requires human review. Extraction → decision → action. Each stage has a defined responsibility. Extraction supplies the values. The decision model evaluates the available options against your policy and context. Your application validates the result and controls execution. That separation matters for agent builders. You can inspect what the model extracted, test the decision independently, and control which actions are permitted. Speed changes where this becomes useful. At roughly 200 ms for extraction, developers can start putting language interpretation directly into interactive software: • A support interface that fills relevant fields while an agent reads a ticket. • A browser agent that extracts values from a message before populating a form. • A find-in-page feature that highlights cancellation deadlines or product complaints, even when the text uses different wording. • A workflow that turns an incoming email into validated tool arguments and evaluates the next action. The opportunity extends to any application where someone currently reads a message, copies information into fields, and decides what happens next. Developers can define the information they need and the decisions their software must make, then connect those outputs to existing functions. We're bringing extraction and decision models together in Sage so builders can implement that flow. Watch the demo below. Register on Levanto by PT today for early access tomorrow.

FAST⚡️ when fast is enough. Slower only when it takes that to get it 🧠 RIGHT. What we learned selling decision models to corporates: - Everyone loves low latency, but in many cases being right matters more. Customers don’t know (or care) whether a question is System 1 or System 2. They’ll happily wait a few hundred extra ms for the right answer. - That’s why @LevantoLabs pioneered reasoning for decision models. Sage answers easy questions in ~100 ms, detects when a question is harder, and reasons only then. - Soon every decision model will expose a latency–accuracy dial. Glide and TypeLLM have already implemented "reasoning". More will come.