Blog · from the Precog Labs team
Short, technical pieces on the thinking behind CRBRL: why compression belongs in the architecture, what storage costs at production scale, and the mathematics that holds it all up.
Bolt-on compression forces a decompress step into the query path. A compression-native design searches the compressed index directly — same retrieval, no unpack, no retraining.
Storage and the I/O around it is 55–80% of a vector-database bill, and it scales linearly with the corpus. As teams move to production, it becomes the planning constraint. CRBRL prices that line like cold storage.
A practical look at the selectable codec layer — when each method fits, and what the trade-offs look like in a disk-persistent index.
How tiering works when the vectors are already 8× smaller, and why the warm tier changes shape once storage stops dominating the bill.
A first look at Compressa, the upcoming product arriving July 2026 that plugs into Claude, ChatGPT, and other models.
// more posts land as the work ships · written by the Precog Labs team
The investor page lays out the market and the economics. The pressroom has boilerplate, releases, and a media contact.