ZON
Zero Overhead Notation
Smart compression for LLMs: 50% fewer tokens than JSON, 100% human-readable. Save ~50% on API costs.
Works with leading AI frameworks & platforms
Native Fluency in ZON
ZON connects your data to LLMs natively. No JSON conversion overhead.
users:@(5):email,id,last_login,tier alice@ex.com,1,2024-12-08,premium bob@site.co,2,2024-11-15,pro carol@demo.org,3,2024-12-01,free dave@net.io,4,2024-10-20,premium eve@corp.ai,5,2024-12-09,pro
{
"users": [
{ "id": 1, "tier": "premium" },
{ "id": 3, "tier": "free" },
{ "id": 5, "tier": "pro" }
]
}Why Choose ZON?
Engineered for the AI era, combining human readability with machine efficiency.
Token Reduction · GPT-4o Total
Token-Efficient Architecture
Achieves ~50% token reduction compared to JSON by utilizing tabular encoding for arrays and minimizing syntax overhead.
Runtime Guardrails
Validate LLM outputs against strict schemas with zero overhead. Type-safe, reliable, and built-in.
99%+ Retrieval Accuracy
Self-explanatory structure with explicit headers eliminates ambiguity, ensuring LLMs retrieve data with near-perfect accuracy.
Streaming Ready
Designed for byte-level parsing, allowing large datasets to be processed incrementally with minimal memory footprint.
JSON Data Model
Maps 1:1 to JSON types including objects, arrays, strings, numbers, booleans, and nulls. Lossless round-tripping guaranteed.
Human-Centric Syntax
Minimal noise, no strict quoting rules, and a clean layout make ZON as readable as Markdown for developers.
Multi-Language Support
Production-ready libraries available for Python and TypeScript, ensuring seamless integration into your stack.