We measured 62% token reduction on academic text with 92% semantic integrity.

  Not a claim. A measurement. Live, today, on our own research papers.                                                                                                                      
                                                                                                                                                                                            
  How it works:
  → Local LLM compresses the prompt
  → Embedding model validates: cosine similarity ≥ 0.90
  → Below threshold? Raw text sent instead. No silent loss.

  This runs as middleware inside CognOS Gateway — before every upstream API call.

  Client → [compress + validate] → OpenAI / Claude / Mistral / Ollama

  40-62% API cost reduction. Semantic integrity guaranteed or fallback.

  Code + methodology:


  #AI #LLM #MLOps #AIInfrastructure #TokenEfficiency

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