Hi {{first_name}}, Most teams building semantic search at {{company}} are quietly burning GPU budget on embedding APIs — and still getting chunked, context-losing retrieval that can't explain itself. The problem: transformer-based embeddings are statistically opaque, GPU-dependent, and degrade on long documents because they require chunking. We built something different. Tejas, our open-source semantic search engine, uses binary phase fingerprints derived from physics — not trained weights — to achieve 5.4M comparisons/second on a single CPU core. No training. No GPU. No black box. We've open-sourced it on Hugging Face so you can benchmark it against your current stack in an afternoon. Worth 20 minutes to see if it changes your infrastructure math? — [Name], Reinforce AI