OrdicaAI @OrdicaAI
AI prompts go in. Up to 50% fewer tokens come out. We find this partially acceptable. https://t.co/LSMNVuJiFX ordica.ai Sacramento, CA. Joined April 2026-
Tweets9
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Followers13
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Following22
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Likes6
We measured the overhead Ordica adds to your API call: 1K tokens: 53ms. 5K: 73ms. 15K: 127ms. 50K: 246ms. Single run. Not a benchmark. A measurement. We found it acceptable and moved on. ordica.ai
@jamiegraytech They won't. Smarter models still bill you for every token you send. Efficiency inside the model doesn't change what leaves your server. Compression before the API call does. That's what we built.
Token savings by workload — including the unflattering ones: RAG: ~50% Conversation history: ~35% Instructions: ~28% Dense JSON: ~9% Blended: ~30% We find honesty more useful than marketing. ordica.ai
The docs are live and, unlike before, correct. We're leaving alpha. Growing pains are likely. Protests will be noted and disregarded. Welcome to the next phase. ordica.ai/docs/
We built this. It's called Ordica. Drop-in proxy for OpenAI, Anthropic, Grok, and Gemini. 1,800+ blind-judged prompts. Quality 4.0+/5.0 on every provider. 82-90% scored equivalent or better. Patent pending. Early access open now. ordica.ai
What if you stripped that redundancy before it hits the API? Same prompt. Same output quality. Up to 29% fewer tokens billed. No rewriting your retrieval logic. No new SDK. No code changes. Swap one endpoint URL. That's it.
We blind-tested 1,800+ prompts across OpenAI, Anthropic, Grok, and Gemini this month. The average RAG pipeline sends 25-35% redundant tokens on every API call. You're paying for whitespace, metadata, and overlapping chunks. Here's where the money goes:
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