~$170B
Projected AI-infrastructure spend by 2029 — the layer CRBRL sits in.
The business case · in plain terms
Every AI feature stores what it knows as vectors — the memory behind its answers. As that memory scales from pilot to production, its cost becomes the line that decides which projects live.
Store 8× more on disk. Retrieve at the same fidelity. Pay a fraction.
For two years, most companies kept AI in a sandbox: small experiments, limited data, a few users. That phase is ending. Teams are now putting AI into the products and workflows their customers and staff rely on every day.
Production means real scale: every document, conversation, and record becomes searchable memory. The amount of data each company stores does not grow by a little — it grows by orders of magnitude. And it keeps growing.
// market figures as cited in CRBRL positioning · the storage share is where CRBRL acts
AI memory is stored at full precision, the way it has been since the technology was young and the datasets were tiny. That default was never revisited. At production scale it becomes the single largest, fastest-growing line in the bill.
The bill rises in a straight line with the amount of data stored — then again for every copy kept for safety, backups, and redundancy. Growth becomes a tax.
The archive worth searching, the assistant worth remembering everything, the agent worth keeping — many never ship, because the storage maths does not work at the size that matters.
Teams cut data, lower quality, or pay more. Bolt-on compression on existing systems forces a slow decompress step on every search — so it was never adopted where it counts.
CRBRL is a compression-native, disk-persistent database for AI memory. It keeps the same vectors using roughly 8× fewer bytes per vector — and it searches the data while it stays compressed, so there is no slow unpack step on every query.
The answers stay effectively identical (≈0.98 cosine fidelity — for non-specialists, that means the results match what you get today). There is no migration to do and no model to retrain. It works on the first record you load.
Your AI memory is stored compressed from the moment it arrives — about one-eighth the footprint.
Search runs directly on the compressed data. That is the part older approaches never solved.
Results match what teams get today. No quality trade for the cost saving.
Compatible with the tools teams already use, including a Postgres extension. No migration, no retraining.
// built on peer-reviewed mathematics — TurboQuant · arXiv:2504.19874
Same data. Same answers. About one-eighth the footprint — on disk, in every copy, in every backup. Pick a size to see the shape of it.
100M records of AI memory · a serious production workload
Footprint is exact (8× fewer bytes per vector). The dollar figure is illustrative — it shows only the raw storage line at ~$0.30 per GB-month across copies. Because storage is 55–80% of the total vector-database bill, the real saving on the full bill is larger.
Adoption is accelerating and moving into production across the board. Every company crossing that line meets the same storage wall within months. The pain is universal, repeatable, and easy to recognise.
No mainstream database solves it at the source. Others cut cost by moving data to cheaper, slower tiers; CRBRL reduces the data itself, then keeps it fast. It is the disk-persistent database built around this peer-reviewed mathematics — a position no one else holds.
Margins stay healthy as usage grows, instead of being eaten by per-customer storage that scales the wrong way.
Years of records can stay searchable and on-site at a footprint that fits the systems security has already approved — not parked in cold storage.
AI that remembers everything becomes affordable to run, because the memory no longer compounds the monthly bill.
When the AI bill grows faster than the business, CRBRL takes the largest line down by roughly an order of magnitude — same quality, same service level.
Pick the door that fits you. Each one leads to a short page and a form — we read every one and follow up to book a call.
Get in touch to discuss your company's AI-memory capabilities — and see whether we can take a serious cut out of your operational burn.
Discuss your workload →AI adoption is compounding — and so is its storage cost, which most teams underestimated by more than half. We solve it at the software layer, before the bottleneck forces a retreat. If that's a wave you want to be on, let's talk.
Explore a partnership →Join the subscription waitlist and register your interest. Exclusive early access, limited for the first little while.
Join the waitlist →CRBRL is a small Precog Labs team shipping a product the whole market is about to need. We care about craft, candour, and people who make the work better. If that's you — in almost any shape — we'd like to meet you.
// hello@crbrl.ai · a Precog Labs product · Sydney + global