CRBRL · COMPRESSION-NATIVE VECTOR DATABASE
/ˈsɛr.ɪ.brəl/ · compression
The first vector database built on peer-reviewed compression mathematics. 8× more on disk. Same retrieval quality. The same database from solo dev to enterprise corpus.
Every vector database stores embeddings the same way: 32-bit floats. The default since the category began, when embeddings were 128-dim and corpora held 100,000 records. It hasn't been revisited.
A modern OpenAI vector is 6,144 bytes. Multiply by your corpus. By replicas. By backups. By snapshots. Storage and IO drive 55–80% of every vector-database bill. CRBRL prices that line item like cold storage — and keeps your retrieval quality where it was.
// 100M vectors @ 1,536-dim · before vs after
Compression at the architecture level changes which AI products are economically viable. Six things that become possible the day you cross 8×.
The compliance archive. The five-year support history. The full source tree. The corpus you couldn't justify before — fits now.
⟶ Same recall · same hardware · 8× the contextAgents that remember every conversation, every tool call, every preference — without the bill compounding monthly. Memory becomes a feature, not a P&L line.
⟶ Memory is the new computePer-tenant vector stores were a margin trap. At 8× density, your worst tenant looks like your best did last year. The unit economics finally close.
⟶ Gross margin you can defendSeven years of embedded support tickets, contracts, communications — at a footprint that lets legal keep them indexed and queryable, not sitting in cold blob storage.
⟶ Searchable retention at archive costThe corpus that needed three nodes now fits on one. Air-gapped, regulated, on-prem deployments become operationally sane — and stay that way as the corpus grows.
⟶ Sovereign · regulated · efficientcrbrl-pg ships as an extension. Same Postgres, same backups, same ops — search runs directly on the compressed index. No new system to operate.
⟶ Drop in. Reclaim the disk.Search runs directly on the compressed index — no decompression in the query path. That is what makes it efficient at scale, and why bolt-on compression in other engines never matched it.
Peer-reviewed mathematics. Near information-theoretic optimal. Works on the first vector you load — no training, no calibration, no embedding-model lock-in.
Every vector enters at 8× density. No staging pass. No second job to run.
Semantic, full-text, hybrid — all running on the compressed index. One API.
Recent data at higher fidelity. Older data compresses further as it ages. Automatic.
Auth, audit logs, multi-tenancy, RBAC. Built in. Not bolted on when customers ask.
Move the buttons. The math runs against the same 8× compression ratio TurboQuant produces — applied to your inputs. Nothing leaves your browser.
384 MiniLM · 768 Gemini · 1,536 OpenAI · 3,072 OpenAI-3-large
→ Same retrieval quality. 8× less footprint on disk, in replicas, in every backup. The line item that used to scale linearly with your data — now compounds in your favour.
CRBRL is the right shape for the teams whose retrieval bill is already a problem — and for the teams whose corpus is about to make it one.
Compliance archives, support history, code, customer comms — storage-heavy, latency-sensitive, audited end-to-end. CRBRL ships as a same-database extension or a standalone vector store.
Already on pgvector? crbrl-pg installs alongside in the same Postgres. Same backups, same ops, same DR story. Search runs on the compressed index — the part bolt-on compressions never solved.
Agents need long-term memory that compounds. Compressed-domain search means more context, lower latency, same hardware. Compatible with Chroma and every major embedding model.
If your vector-DB bill is growing faster than your business, CRBRL drops the storage line by an order of magnitude. Same recall, same SLA — the operational economics finally close.
Pilot partners get every line below — with the next two columns landing before they hit the public roadmap.
Get ready to give your AI capabilities superhuman performance — and plug it straight into Claude, ChatGPT, and the models you already run. The next release from the team behind CRBRL.
Three ways to get started. Pick the one that fits.
▲ 1,427 on the whitelist · 8 of 12 pilot spots remain · partnerships open
// not sure which fits? Email hello@crbrl.ai and we'll route you.