Industries · AI & Data Companies
Infrastructure that keeps up with your models — not the other way around.
GPU cluster management, ML pipeline automation, data platform engineering, and production AI ops for companies where the infrastructure is as critical as the model itself.
Pain points we hear most often
Training runs that block product delivery
Long-running jobs consuming shared capacity, no preemption strategy, and no visibility into utilisation. We design GPU fleet management with job queuing, spot-instance fallback, and cost attribution per experiment.
Data pipelines that break silently
A failed batch job that produces stale features is worse than an obvious outage — the model degrades without an alert firing. We instrument pipelines for data freshness, schema drift, and volume anomalies.
Model serving that can't handle real traffic
Inference endpoints that held up in testing fall apart on production traffic patterns. We design autoscaling inference infrastructure with latency SLOs and graceful degradation.
Who we work with in AI & Data Companies
AI-native startup with a production model
Model works in the notebook; now you need to run it reliably at scale, version it, and update it without taking the product down.
Enterprise adding AI capabilities to existing products
Running LLMs or fine-tuned models alongside legacy services, with security and data governance requirements the AI team wasn't hired to solve.
Data platform team building for the whole company
Centralised data infrastructure with quality, lineage, access control, and a backlog longer than the team can run.
Research-to-production bridge
Research org producing models; product team needing to ship them. The gap between the two is usually an MLOps problem we know well.
Frequently asked questions
How do you approach GPU cost management? +
Utilisation dashboards per team, spot/preemptible instance strategies with checkpoint-and-resume, and reserved capacity for steady production inference workloads. Typical savings versus naive on-demand: 50–70%.
Can you help us build an MLOps platform? +
Yes — experiment tracking (MLflow or Weights & Biases), model registry, automated retraining pipelines, and deployment workflows with canary and rollback. We build it on your cloud, under your control.
How do you handle data governance for AI training data? +
Access control, lineage tracking, and retention policies at the storage layer. We integrate with whatever catalogue you use (Datahub, Alation, or a lighter homegrown solution).
What's your approach to LLM inference infrastructure? +
vLLM or equivalent for throughput, autoscaling against token-per-second metrics, and latency SLOs at the p95/p99 level. For cost-sensitive workloads, we model spot vs reserved vs serverless GPU tradeoffs.
Can you help us with vector database infrastructure? +
Yes — Pinecone, Weaviate, Milvus, or pgvector on managed Postgres. Selection and sizing depend on your query patterns, update frequency, and scale. We've operationalised all four.
How do you secure AI model artifacts and training data? +
Encrypted storage, least-privilege access policies, audit logs on all data access, and network isolation for training jobs. Model artifacts are treated the same as production binaries.
What about feature stores? +
We've implemented Feast and Tecton in production. The key architecture decision is online vs offline latency requirements — we help you pick the right shape and avoid over-engineering it.
Can you help with AI observability (model drift, data drift)? +
Yes — embedding monitoring for drift detection, prediction distribution tracking, and alerting tied to your incident management workflow. We treat model health as a production SLO.