Verifiable Storage for Physical AI
Your sensor data is the scarcest resource in AI. Store it on infra that keeps the upside yours with zero egress, customer-controlled keys, cryptographic provenance.
The Physical AI Data Problem
Your sensor data is the product.
Traditional cloud treats it like a tax.
Traditional cloud treats it like a tax.
Physical AI data: robotics, satellite, sensor, neurotech, is captured once and increasingly an asset on your balance sheet. The architecture you store it on decides who keeps the upside.
On a hyperscaler, your data sits on infrastructure operated by your customers' competitors and exits through metered pipes. Every delivery is a tax. Keys and provenance are bolted on.
Akave inverts this. Egress is zero, so distribution doesn't cost you margin. Every object carries a cryptographic proof of integrity an, so buyers verify provenance independently.
On a hyperscaler, your data sits on infrastructure operated by your customers' competitors and exits through metered pipes. Every delivery is a tax. Keys and provenance are bolted on.
Akave inverts this. Egress is zero, so distribution doesn't cost you margin. Every object carries a cryptographic proof of integrity an, so buyers verify provenance independently.
Built for Data Monetization
Physical AI is a data business. We built the storage to match.
Edge Data Capture
Capture once. Preserve the value forever.
Ingest image, video, telemetry, and sensor data
Store data through an S3-compatible interface
Preserve provenance from the first write
Resilient Data Protection
Protect recovery data from encryption, deletion, and tampering during attacks.
Immutable, write-once backup, object-lock copies
Cryptographic proof of object integrity
Recovery without egress penalties
Secure Data Monetization
Turn proprietary datasets into revenue without giving away control.
Verified provenance for buyers and partners
Unlimited restore and access
Realistic ransomware drills without penalties
Compliance-Ready
Make every critical dataset audit-ready by design.
Transparent data history across the lifecycle
Tamper-evident records for audits
Object-level integrity verification
Why Physical AI Teams
Choose Akave
Explore real-world case studies to see how leading teams integrate Akave into their data infrastructure.
Provable Satellite Observations Datalake
The sky is getting busier - more satellites, more debris, more transient events, and more eyes on the sky than ever before. SkyMapper is turning that complexity into a shared observatory. Hundreds of telescopes and all-sky cameras around the world now feed into a single, verifiable record of what the sky looked like at a given moment. Akave Cloud is the storage backbone that makes those observations provable instead of just plausible.
Building Verifiable Edge Intelligence
375ai, a decentralized edge data intelligence network, has integrated a technical pilot with Akave, an enterprise-grade, S3-compatible object storage solution purpose built for edge and AI. Akave provides 375ai with the data integrity guarantees, low latency, high performance, and significant cost savings.
From Edge Query
to Onchain Receipts
Edge pipelines multiply. Transport becomes the product. Discovery becomes prefix browsing. Akave × AnyLog replaces complex pipelines with an in-place system, query distributed data with SQL, offload to storage with onchain receipts.
Save up to 80% and monetize your Physical AI data
Akave helps Physical AI teams reduce storage costs by up to 80% while making high-value real-world datasets easier to move, verify, license, and reuse.
Store data once, then use it across AI training, analytics, customer delivery, and data marketplaces without egress penalties. Every object can carry cryptographic proof of integrity, helping buyers trust the data and helping you keep the upside.
Store data once, then use it across AI training, analytics, customer delivery, and data marketplaces without egress penalties. Every object can carry cryptographic proof of integrity, helping buyers trust the data and helping you keep the upside.
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