Stop overpaying for compute.
The ARM of AI compute.
Hadron is a patent-pending runtime that routes every AI and compute workload to the cheapest hardware that can actually run it, across cloud, edge, on-prem, mobile, and the user's own browser. No data leaves your systems.
A drop-in library that picks where your computing runs.
Hadron lives inside your software. It profiles each workload, picks the cheapest capable hardware to run it on, and runs it there. Set it to save money by default, or to go faster when you need to.
It is a library, not a service. Nothing is sent to us, and no data leaves your environment.
The hardware already exists. The intelligence to route to it did not.
To run AI and heavy compute, companies pay the cloud bill or buy more servers, and watch it eat their margins. There has been no real alternative.
Meanwhile, hyperscalers spent over $450B chasing compute they do not have, while billions of capable GPUs sit idle in customers' pockets, already paid for.
Three steps, on every workload.
Look
Hadron reads the shape of the job: its size, type, and precision needs. Never the data itself.
Pick
It scores the options and chooses the cheapest hardware that can actually do the job, wherever that is.
Run
It runs it there, and falls back safely if a target is unavailable. Optimise for cost by default, or flip to speed.
One library, the full spectrum of compute.
Hadron picks the cheapest capable target every time.
Cloud
Hyperscaler GPU
Edge
CDN and serverless
Client browser
WebGPU, on 5 billion devices
Mobile native
iOS Metal and Android Vulkan, 2 billion-plus devices
Enterprise
CUDA and DirectCompute on-prem
Specialised silicon
NPU, TPU, and embedded
Everything else optimises one workload in one place.
ONNX Runtime Web is static. TVM and WebLLM are compile-time. Tools like gpu.js are primitives. None of them decide, at runtime, where a workload should actually run. Hadron is the routing layer across all of them, built on:
Deterministic scoring
A transparent formula, not a black-box learned model. You can see why every routing decision was made.
GPU buffer retention
Consecutive GPU steps stay resident instead of paying to move data back and forth between CPU and GPU.
GPU inhibition
Categorically blocks workloads that run slower on the GPU, so the runtime never makes a job worse.
Transparent CPU fallback
It never breaks if hardware is unavailable. The workload still runs, just on the next best target.
Verified at scale.
a working runtime anyone can use right now
failures across 447 production tests
peak sorting speedup vs standard JavaScript
peak search speedup vs a CPU baseline
- 18 production-grade demonstrations across 9 sector verticals.
- Modelled saving of over £10M a year for a Tier 1 customer at scale (modelled from measured benchmarks, not yet realised in production).
Speedups are peak figures, and routing is configurable for cost or speed.
We own the routing decision.
ARM does not make chips. It owns the way chips are organised, and every chipmaker pays a fee per chip.
Hadron does not run AI. We own the routing decision, and every runtime that ships routing logic can pay a fee per workload routed.
The only filed IP in cross-platform compute dispatch.
Five UK patent applications, all assigned to Ayoob AI Ltd, filed with the UKIPO in 2026 (examination pending).
Platform GPU Inhibition
Cross-platform routing core
Adaptive Compute Allocation
ayoob-compute
Accelerated Query Processing
ayoob-query
Accelerated Search Operations
ayoob-search
Adaptive Sorting Engine
ayoob-sort
Anyone renting compute their users already own.
- AI platforms where server compute keeps margins negative.
- Browser-heavy SaaS where every search, sync, and render is billed back as cloud compute.
- Data and analytics tools where every dashboard refresh is paid compute.
- Media and rendering tools where every export runs in the cloud.
- Every web app swapping in from CPU-only libraries.
5 billion devices became programmable overnight.
WebGPU shipped across every major browser, Chrome, Edge, Safari, and Firefox, in Q4 2025. It is an open W3C standard, not vendor-controlled.
Five billion devices became programmable for GPU compute overnight, and Hadron is the layer that puts them to work.
See it run.
Hadron is live and working today. Try the demo, or get in touch to talk about your workloads.