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AI Infrastructure
We design and operate the platform layer beneath your AI systems: inference serving, orchestration, evaluation pipelines, and cost controls. Everything is built to be measured — every model call has a latency, a cost, and an owner.
When you need this
The situations we are called into.
GPU or API spend is growing faster than usage and nobody can attribute it to teams or products.
Every team has built its own model-serving stack, and none of them is on call for it.
Data-residency or sovereignty requirements rule out the default cloud offering.
One product's AI feature is becoming a platform that must serve many.
Approach
How the engagement runs.
Audit and load profiling
Current workloads, latency distributions, and spend measured before anything is proposed.
Platform architecture
Serving, gateway, queueing, and evaluation layers sized to your workloads and residency constraints.
Build
Infrastructure as code, a unified model gateway, and observability wired in from the first deployment.
Migration
Teams moved onto the platform workload by workload, with rollback available at every step.
Operations
SLOs, on-call procedures, capacity planning, and cost reporting — run by us or handed over.
Deliverables
What you hold at the end.
Platform architecture and IaC
Unified model gateway
Evaluation and observability pipelines
Cost-attribution model
Operations runbook and SLOs
Related research
The evidence behind this practice.
Benchmark · 2026
Private LLM inference on Apple-silicon clusters
Throughput, latency, and cost characteristics of quantised open-weight models served on commodity Apple-silicon hardware, measured against cloud baselines.
In preparation
Contact
Talk to our engineers.
Describe the situation you are in. The person who replies is the person who would do the work.