Client Cloud Platform
Your AI initiative stalled
because the infrastructure
was never designed for it.
We design, build, and operate the GPU clusters, ML pipelines, and orchestration systems that production AI workloads actually require. No vendor lock-in. No generic cloud defaults.
Module 01 — What We Build
Three problems.
One engineering firm.
Most organisations reach us after a failed cloud migration, a model that won't scale, or an infrastructure bill that tripled without explanation. We fix the root cause.
// 01 — Compute
GPU Infrastructure Design
We spec, procure, and configure GPU clusters — bare metal or cloud — built around your actual model workload, not a vendor's recommended tier. From H100 clusters to multi-node A100 arrays.
Explore compute →// 02 — Pipelines
ML Pipeline Orchestration
Training runs, fine-tuning loops, and inference serving each require a different throughput profile. We build the orchestration layer that keeps them from competing for the same resources.
Explore pipelines →// 03 — Systems
Enterprise AI Architecture
From model registry to production monitoring, we design the full system — including the parts your data science team didn't know they needed until something broke at 3am.
Explore architecture →Module 02 — Live Platform
What the control
plane looks like.
Every deployment we run is managed through our orchestration CLI. Your engineering team gets full read access from day one — no black box, no support tickets to see your own cluster state.
- Real-time GPU utilisation per node
- Job queue depth and priority lanes
- Fault detection with automatic rerouting
- Cost attribution by workload type
01 / Sector Deployments
Where we've built.
These are active engagements — not hypotheticals. Each one required custom infrastructure built for the operational constraints of that specific industry.
01 // Agriculture
Crop Monitoring Infrastructure
We deployed a network of low-power compute nodes across a regional farming operation to collect soil, moisture, and yield data in real time. The output feeds a forecasting model that helps agronomists make irrigation and harvesting decisions several weeks ahead of the traditional calendar approach.
02 // Pharmaceutical
Production Line Traceability
A mid-size pharmaceutical manufacturer needed an auditable record of every step in their production process — batch origin, handling conditions, QA checkpoints — without disrupting existing line equipment. We built a lightweight logging layer that runs alongside production and generates audit-ready records automatically.
03 // Logistics
Fleet Maintenance Automation
A transport operator was managing workshop scheduling on spreadsheets while their vehicles were generating detailed onboard diagnostic data that nobody was reading. We built the pipeline that connects vehicle telemetry to workshop management — surfacing faults before they become breakdowns and automating parts ordering based on real usage patterns.
04 // Energy
Renewable Grid Stabilisation
Renewable generation is inherently variable. A regional grid operator asked us to build a system that continuously models supply and demand across their network and makes small, automated adjustments to balance load — reducing the amount of expensive standby capacity they need to hold in reserve.
02 / Core Architecture
How our systems
are built.
Every Hyperion deployment shares the same foundational design principles. These are the baseline requirements we hold every engagement to before a system goes live.
01 / 03
Predictable Resource Usage
Our runtimes are designed for consistent, low-overhead operation. We eliminate the garbage collection spikes and memory fragmentation that cause unpredictable slowdowns in long-running industrial processes — because in operational environments, a paused system isn't just an inconvenience.
Memory-safe02 / 03
On-Device Processing
Where latency matters, we process data at the source rather than routing it through a cloud endpoint. This removes the network as a point of failure and keeps response times consistent regardless of connectivity conditions — critical for machinery, vehicles, and field equipment.
Edge-native03 / 03
Offline-Capable Deployment
All Hyperion systems are designed to operate without an internet connection. This matters in secure facilities, remote sites, and air-gapped regulatory environments. There is no dependency on external APIs or cloud services that could break access to a running system.
Air-gapped readyModule 03 — Engagement Model
How an engagement
actually works.
We don't do discovery workshops that produce slide decks. Every engagement starts with a read of your existing infrastructure and ends with something running in production.
Step 01 / 04
Infrastructure Audit
We review your current compute setup, cloud spend, and model workload profiles. You get a written findings document within 48 hours. No charge for the audit.
Step 02 / 04
Architecture Design
We produce a detailed system design — cluster topology, orchestration logic, networking, storage — specific to your workload. Not a template. Reviewed line by line with your team.
Step 03 / 04
Build & Deployment
We build and deploy the system. Your team is in the room throughout. The first 30 days of operational support are included in every engagement.
Step 04 / 04
Handover or Retain
At 30 days, you choose: full handover with documentation and training, or retaining us on an ongoing operational basis. Both options are clean and defined upfront.
Start Here
Request an infrastructure audit.
Tell us what you're running, what's breaking, and what you're trying to do. We'll come back with an honest read of your situation — not a sales pitch.