$ dottxt generate \
--model Qwen/Qwen3.5-27B \
--prompt "Is this output valid?" \
--schema '{"valid": "boolean"}'
{"valid": true}01By the team that pioneered structured outputs
NoBadOutputs
No
Bad
Outputs
Reliable Agent Infrastructure
02They use our technology
"Structured generation is the future of LLMs.”
Julien Chaumond, Cofounder & CTO, HuggingFace
03From tokens to agents
Reliability, at every layer of the stack
Production AI needs more than models that usually work. It needs contracts that always hold. From the tokens a model emits, to the agents that compose them, to the specifications those agents fulfill.
This is what we build.
Structured outputs. Per-call reliability. Your LLM's output matches your schema, every time. No retries, no validation loops, no defensive parsing. JSON Schema, regular expressions, context-free grammars; whatever shape your system needs, the model produces it by construction.
04Solutions
04.1
api.dottxt.ai
123
Serverless API with the most reliable and extensive structured outputs solution, and function calling. Streaming, batching available. Multiple open-source models available.
04.2
For teams that self-host
dotjson is our high-performance library for constraining LLM output to match a predefined JSON Schema. Available under a commercial license with a Python, C, C++, or Rust API.
Use cases: agent protocols, information extraction, annotation, synthetic data, function calling.
04.3
For inference providers
dotregex is our high-performance library for constraining LLM output to match a predefined regular expression. Available under a commercial license with a Python, C, C++, or Rust API.
Use cases: information extraction, table generation, classification
05About .txt
We make AI behave like software
New rules for AI
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 contracts, their outputs don’t compose, they just accumulate noise. Despite their tremendous capabilities, AI systems built on LLMs remain unrealized potential.
That’s why we built .txt: to make AI composable.
We believe that safety and reliability are the foundation. That contracts are what let systems scale. That AI should behave like software.
So we build reliability at every layer of the stack: from the tokens a model emits, to the agents that act on them.
AI is the new software. We make sure it acts like it.