Multi-layer AI request optimization that reduces token costs by 90% through intelligent preprocessing, semantic caching, and context-aware compression.
AROL combines data minimization, semantic caching, and intelligent monitoring to deliver unprecedented efficiency.
Strips EXIF metadata from images, resizes to model-native dimensions, converts to WebP format, and removes social pleasantries from text prompts. Typical reduction: 5MB images to 50KB, 2000 tokens to 800 tokens.
Uses perceptual hashing (pHash, dHash) for images and transformer-based embeddings for text. Approximate nearest neighbor search with 85-95% similarity thresholds delivers sub-100ms cache lookups.
Real-time logging of pre/post optimization metrics with automated patent-evidence report generation. Track compression ratios, cache hit rates, and cost savings across all request types.
Intelligent context window management with semantic chunking, relevance scoring, and memory tiering. Solves the "lost in the middle" problem through intelligent ranking and compression.
Raw multimodal data enters the optimization pipeline
Domain-specific compression and social signal removal
Semantic fingerprinting and similarity matching
Only unique requests hit the API. Others return cached.