Engineering discipline for production AI.
MLOps, AIOps, and cloud compute optimisation for AI workloads running in regulated, sovereign environments.
For fifteen years, LimePoint engineers have made complex systems run faster, cheaper, and more reliably — engineering data platforms, APIs, and the Kubernetes, AWS, and Azure infrastructure they run on under real production load. AI workloads now demand the same discipline, and the economics make it urgent: GPU capacity is scarce, inference costs compound, and most production AI systems are poorly instrumented and wastefully provisioned. MLOps and AIOps tooling address the surface; the harder work — cloud compute optimisation, sovereign deployment, and production reliability — sits underneath. As new Australian data centre capacity pushes regulated AI workloads on-shore, enterprise and government need partners who can engineer that layer properly, not just integrate APIs. We apply the rigour we've always brought to Data, API, and Platform engineering, now to AI.
Capabilities
Cloud Compute & AI Workload Optimisation
GPU utilisation in most production AI deployments sits well below what the hardware can deliver, and the cloud compute underneath it — Kubernetes clusters, AWS and Azure instance fleets — is consistently overprovisioned and under-scheduled. Inference costs scale linearly with poor batching, naive model serving, and unoptimised placement across instance types. We profile workloads end-to-end — model selection, quantisation, batching strategy, Kubernetes scheduling, AWS and Azure instance and GPU selection, autoscaling behaviour — and rebuild the hot paths. The same cloud compute optimisation discipline we bring to Data, API, and Platform engineering, now applied to transformer inference, vector search, and distributed training.
Sovereign AI Infrastructure
Regulated workloads in banking, utilities, telco, and government increasingly cannot run in US hyperscaler regions. Australian data centre capacity is expanding, but standing up production AI infrastructure on-shore — model hosting, GPU clusters, private networking, control-plane isolation — requires engineering depth most organisations lack internally. We design and operate sovereign AI platforms that meet APRA CPS 234, IRAP, NERC CIP, and IEC 62443 requirements without compromising on performance or developer experience.
AIOps, Reliability & Observability for AI Systems
AI systems fail differently. Non-deterministic outputs, silent quality drift, token-level latency spikes, and degradation under load don't show up in traditional APM tools. We build AIOps observability stacks that instrument the behaviours that actually matter — prompt and response telemetry, evaluation pipelines, GPU and memory signals, cost-per-request tracking — and wire them into the same SRE practices we've used to keep critical trading, billing, and customer platforms running.
MLOps & AI Platform Engineering
MLOps tooling — model registries, evaluation harnesses, inference gateways — only delivers value when it is integrated into a coherent platform. Most enterprises are losing time and money to fragmented tooling, unclear ownership, and no consistent path from experiment to production. We build internal AI platforms that give engineering teams paved roads: model gateways, managed inference endpoints, evaluation pipelines, deployment automation, policy controls. Integrated with OpsChain where change governance matters, so model and prompt changes move through the same controlled pipelines as the rest of your regulated infrastructure.
The engineering heritage we bring to AI
- Fifteen years of Data, API, and Platform engineering across Kubernetes, AWS, Azure, and on-premise infrastructure — in production environments where every millisecond of latency and every dollar of compute cost is measured and defended.
- Long-standing delivery partner to regulated Australian enterprises across banking, utilities, telco, and government — the same sectors now being forced to bring AI workloads on-shore.
- Engineering teams whose core skill is the deeply technical work most consultancies cannot staff: profilers, flame graphs, kernel tracing, GPU and memory analysis, capacity modelling, failure-mode reasoning.
- Builders of OpsChain — our change management and orchestration platform running in regulated production environments.
- Australian-owned and Australian-delivered, with engineers who work on-shore and on-site where sovereignty, security clearance, and physical presence matter.
Put real engineering behind your AI workloads.
Talk to an engineer who has spent a career making expensive systems fast, reliable, and cheap.