Every powerful system begins the same way: with rules. Rules as a language, a protocol, a way for parts to fit together into something greater The history of software is really a history of this idea: from UNIX pipes to API contracts to typed programming languages, composability has always been the path to scale.

But large language models broke that pattern. They’re powerful, but unpredictable. Flexible, but fragile. And without structure, their outputs don’t compose, they just accumulate noise. Despite their tremendous capabilities, LLMs remain unrealized potential.

That’s why we built .txt: to make LLMs predictable, programmable, and production-ready. We give developers the tools to define exactly how LLMs should respond, using clear, typed schemas that turn loose generations into reliable outputs. Our technology, structured generation, helps teams design safer, more reliable AI outputs from the ground up. Not by patching errors after they happen, but by preventing them entirely.

The result? Fewer mistakes, less wasted compute, and systems that finally feel like software: callable, composable, and testable like any other part of your stack.