CineMatch AI runs TurboQuant in browser

More interesting than the compression ratio is the product shape it enables: browser-native retrieval that behaves like a tiny vector database. The demo is compelling, but the real test is whether the same approach stays fast and accurate once the catalog grows beyond a curated movie set.

• –6x compression cuts 384-dim float32 embeddings from 1,536 bytes to 249 bytes, which is dense enough to make client-side retrieval practical.
• –Keeping the full vectorized index around 12 KB and scoring top-k matches in about 13 ms fits cleanly inside a 60fps browser budget.
• –The direct WebAssembly SIMD dot-product path is the real engineering signal here: compressed vectors are queried in place instead of being decompressed first.
• –For privacy-first recommendation apps, this is a strong pattern, because the user’s preference data never needs to leave the device.
• –The caveat is scope: this is a focused demo, not evidence yet that the same latency and recall hold on larger, messier catalogs.