We build open-source tools that maximize the signal in every AI interaction. More context, less noise, faster responses, and sharper results.
Every token you send to a model should carry meaning. But today's formats bury your data in syntax overhead (braces, tags, repeated keys), drowning the signal in noise and leaving less room for the context that matters.
Verbose formats fill your context window with noise. The more space wasted on syntax, the less room your model has for the information that actually drives better results.
Bloated payloads mean longer round-trips. Leaner data gets to the model faster and comes back sooner, making your entire pipeline more responsive.
Research shows LLM accuracy drops 10–30% as inputs grow. When syntax eats your context window, your model misses the information it needs to reason well.
What we're building toward
We believe every interaction with AI should be as clear and information-dense as possible. SaveTokens exists to close the gap between the data your models need and the bloated formats we currently send them.
We're building a family of open-source tools, each targeting a different source of noise in the AI pipeline. Better formats, smarter compression, tighter integrations, all designed to help you communicate more, in fewer tokens.
Three principles behind everything we build
Every token should carry meaning. We build tools that strip away syntax noise so your models see more data and less formatting.
Leaner communication can't mean lossy communication. Every tool we ship includes type safety, validation, and guarantees that your data arrives intact.
All our tools are MIT licensed and community-driven. Better AI communication benefits everyone, not a competitive advantage to hoard.
Production-grade libraries that make every AI interaction leaner, faster, and more precise.
Adaptive eXchange Oriented Notation
The data serialization format built for LLMs. Feed it JSON, XML, or any verbose format. AXON produces output that's 60–95% smaller, fully typed, and schema-validated, so your models see more signal and less noise.