We architect, deploy, and operate AI systems for enterprises — in the cloud, on‑premises, and across both. The same reference‑architecture discipline from first pilot to production.
Where your AI runs should follow your data, your compliance posture, and your economics — not the other way around. We design for the deployment model that fits, and keep the architecture consistent across them.
Architecture and tuning for hyperscaler and neocloud platforms, built to stay efficient and portable as usage grows.
When residency, sovereignty, or latency require it, we design the hardware and software together on OEM-validated reference architectures.
AI systems fail at the seams — between compute and fabric, fabric and storage, platform and model. We design every layer, so the seams are engineered rather than discovered.
Accelerator selection, node and rack design, NVLink domain sizing, and power and thermal budgets matched to the workload.
Rail-optimized leaf–spine topologies over 400/800G Ethernet with RoCEv2 or InfiniBand — congestion control, cabling plans, and fabric telemetry included.
Parallel filesystems and NVMe-oF sized for dataset staging and checkpoint bandwidth, so storage never gates the GPUs.
Kubernetes with GPU scheduling and partitioning, multi-tenancy, observability, and the runbooks to operate it day two.
Inference engines with continuous batching, quantization, and KV-cache management — throughput and latency engineered per dollar, and measured.
Distributed strategies across data, tensor, and pipeline parallelism, with reproducible runs and honest utilization numbers.
The pilot runs the same architecture as production — smaller. Scaling means adding capacity, not redesigning.
Map the workloads, data constraints, and economics before a single spec is drawn.
A pilot design — cloud, on-prem, or hybrid — engineered from day one to scale cleanly.
Deploy on real workloads. Validate performance, cost, and compliance against agreed acceptance criteria.
Grow into production by adding capacity — same architecture, same operating model.
Acceptance criteria are agreed in writing before we build — throughput, latency, cost per unit of work — and the pilot is measured against them.
Environments where residency, auditability, and predictable performance are requirements — not preferences.
Clinical and research workloads with strict residency and privacy controls.
Low-latency inference with the audit trails and controls regulators expect.
Edge and plant-floor inference for quality, throughput, and predictive operations.
Sovereign deployments aligned to procurement and security frameworks.
Forecasting and asset intelligence deployed close to operational systems.
Demand forecasting, search, and personalization from pilot site to fleet.
Aetheon AI is a team of infrastructure engineers and AI architects. We have designed, deployed, and operated production AI systems across hyperscale, neocloud, and enterprise environments.
We work with enterprises bringing AI in-house, with GPU cloud providers building their platforms, and with teams already operating at scale. The engineers who design your system are the ones who build and commission it.
A few lines on your workloads, deployment model, and constraints is enough to start.
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