Module 02 — Capabilities
What we actually build.
Four capability areas. Each one is a discrete discipline — we staff engagements with engineers who specialise in that specific area rather than generalists who cover everything loosely.
01 / Compute
GPU Cluster Design & Provisioning
We design and deploy GPU infrastructure sized to your actual workload — not the closest available cloud tier. That distinction usually saves 30–60% in monthly compute spend.
What this includes
- Bare-metal server selection and rack design for owned infrastructure
- Cloud-based GPU fleet configuration across AWS, GCP, and Azure
- Multi-node interconnect design using InfiniBand or RoCE
- NVLink and NVSwitch topology for H100/A100 multi-GPU nodes
- Storage architecture: NVMe local, NFS, and distributed object storage
- Cooling, power, and physical capacity planning for on-premise builds
02 / Pipelines
ML Pipeline Orchestration
Training, fine-tuning, and inference are three different jobs with three different resource profiles. We build the orchestration layer that handles all three without them competing for the same GPUs.
What this includes
- Training job scheduling with priority queues and preemption policies
- Distributed training across multiple nodes using PyTorch DDP or FSDP
- Fine-tuning pipelines with checkpoint management and experiment tracking
- Inference serving with autoscaling based on request volume
- Batch inference pipelines for high-throughput offline workloads
- Model versioning, A/B routing, and canary deployment logic
03 / Observability
Infrastructure Monitoring & Alerting
You can't manage what you can't see. We instrument every deployment with the metrics that actually matter — GPU utilisation, job throughput, memory pressure, and cost per workload.
What this includes
- GPU and CPU utilisation dashboards with per-job attribution
- Memory pressure and thermal monitoring with automated alerting
- Job queue depth, wait times, and scheduling efficiency metrics
- Cost tracking broken down by team, project, and workload type
- SLA monitoring for inference endpoints with latency percentile tracking
- Incident detection and on-call runbook integration
04 / Architecture
End-to-End AI System Design
For organisations building AI products rather than running one-off experiments, we design the full system: data ingestion, model lifecycle, serving layer, and the operational processes around all of it.
What this includes
- Data pipeline design from source to training-ready format
- Model registry and artifact management
- Feature store design and online/offline serving split
- Shadow deployment and evaluation framework setup
- MLOps process design: retraining triggers, validation gates, rollback
- Security review: model access controls, audit logging, data handling
Engagement Structure
How engagements are structured.
Audit
No charge
A detailed read of your current infrastructure with written findings and specific recommendations. No strings attached.
- Infrastructure review and findings document
- Compute cost analysis
- Architecture gap assessment
- Written recommendations report
Build
Engagement-based
Full design and deployment of your infrastructure, with your team involved throughout and 30 days of operational support included.
- Everything in Audit
- Full architecture design document
- Hands-on build and deployment
- 30 days operational support
- Runbooks and handover documentation
Retain
Monthly
Ongoing infrastructure management, monitoring, and on-call engineering support after your initial deployment.
- Everything in Build
- Ongoing cluster management
- Incident response and on-call
- Monthly infrastructure review
- Priority access for new work