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Shelby
Verifiable data infrastructure for distributed compute
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0.8MB · 7,554px tall · uses GT Planar-df43b77c23afa18e, ABC Repro Mono-d4db1778d6902b9e
Industry
AI Infrastructure, Data, Web3 Crypto
Style
Dark Mode, Gradient, 3D, Typographic
Stage
Series A
Built with
Unknown
About the site
Shelby is a verifiable data platform for distributed compute, built for workloads like model training, robotics, and agentic systems. Expansive typography, dimensional graphics, luminous color, and a strong sense of movement give storage infrastructure an unusually vivid identity. A useful reference for infrastructure brands wanting a bold presence without obscuring the product.
How this page sells
For ML platform leads tired of moving data between clouds: one problem, one diagram, then Jump Trading and Aptos credibility.
Primary navigation
- Why Shelby
- Features
- Use Cases
- Talk to Us
ClickTap a section to see it on the page
'The Verifiable Data Platform for Distributed Compute' in heavy black type beside a bold pink abstract mark, with 'Use the compute that fits each workload without moving and copying your data every time it changes' and workload tags.
Why it converts: The subhead names a costly daily chore (copying data to each new compute provider), and the workload tags let an ML lead confirm fit in a glance.
Open Ledger, NBCUniversal, Watchee, Flashback Labs and PathPulse.ai in a rounded strip.
Why it converts: A media giant beside AI startups shows the platform handles both heavy media workloads and AI pipelines.
'Your data layer shouldn't limit your compute choices.' set large on its own under a small 'The problem' label.
Why it converts: Framing storage as the thing that locks teams into one cloud turns an infrastructure purchase into freedom of choice, a message CTOs care about.
'Your data, available to the compute you choose.' on dark brown, with a diagram: Shelby Storage & Cache regions on the left, a pink Shelby APIs 'Single Address' in the middle, training and inference providers on the right.
Why it converts: A single diagram shows exactly where Shelby sits between storage and compute, answering the architecture question before a technical evaluation.
Three cards (one address with a world map of regions, fewer copies to manage, a cryptographic record of every upload) then an accordion: S3-compatible, designed to scale, unmetered movement, built for your environment.
Why it converts: 'S3-compatible' removes the integration fear, and 'unmetered movement' targets the egress fees that make multi-cloud expensive.
Four line-drawing cards: Agentic Systems, Robotics & Physical AI ('collected in one location, processed in another and trained in a third'), Model Cache & Checkpoints, Distributed Training.
Why it converts: Each card describes a real data pipeline, so a robotics or training team can recognise their own workflow and the pain it removes.
A pink panel: 'Engineered by Jump Trading Group's high-frequency systems and Aptos' global infra experience', with both names underlined as links.
Why it converts: High-frequency trading engineering is a byword for speed and reliability, which lends a new infrastructure brand instant technical credibility.
'Let's talk about your data.' with 'One address, global access. Cryptographic record from the start.' and Talk to us, beside the pink mark.
Why it converts: The close repeats the two differentiators in one line, so the reader leaves with the pitch they'd forward to their team.
