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View All on GitHubUnified compression pipeline for LLM inputs: trim irrelevant rows and fields, re-encode the rest in the cheapest lossless format (TOON / JSON / CSV, measured on your tokenizer), and account for every token saved with an audit trail of what was removed. 95% fewer tokens on realistic payloads, 100% needle recall.
Unified compression pipeline for LLM inputs: trim irrelevant rows and fields, re-encode the rest in the cheapest lossless format (TOON / JSON / CSV, measured on your tokenizer), and account for every token saved with an audit trail of what was removed. 95% fewer tokens on realistic payloads, 100% needle recall.
Unified compression pipeline for LLM inputs: trim irrelevant rows and fields, re-encode the rest in the cheapest lossless format (TOON / JSON / CSV, measured on your tokenizer), and account for every token saved with an audit trail of what was removed. 95% fewer tokens on realistic payloads, 100% needle recall.
Unified compression pipeline for LLM inputs: trim irrelevant rows and fields, re-encode the rest in the cheapest lossless format (TOON / JSON / CSV, measured on your tokenizer), and account for every token saved with an audit trail of what was removed. 95% fewer tokens on realistic payloads, 100% needle recall.
Unified compression pipeline for LLM inputs: trim irrelevant rows and fields, re-encode the rest in the cheapest lossless format (TOON / JSON / CSV, measured on your tokenizer), and account for every token saved with an audit trail of what was removed. 95% fewer tokens on realistic payloads, 100% needle recall.
Unified compression pipeline for LLM inputs: trim irrelevant rows and fields, re-encode the rest in the cheapest lossless format (TOON / JSON / CSV, measured on your tokenizer), and account for every token saved with an audit trail of what was removed. 95% fewer tokens on realistic payloads, 100% needle recall.
Unified compression pipeline for LLM inputs: trim irrelevant rows and fields, re-encode the rest in the cheapest lossless format (TOON / JSON / CSV, measured on your tokenizer), and account for every token saved with an audit trail of what was removed. 95% fewer tokens on realistic payloads, 100% needle recall.
Unified compression pipeline for LLM inputs: trim irrelevant rows and fields, re-encode the rest in the cheapest lossless format (TOON / JSON / CSV, measured on your tokenizer), and account for every token saved with an audit trail of what was removed. 95% fewer tokens on realistic payloads, 100% needle recall.