ML Infrastructure
Training, evaluation, and inference platforms tuned for cost, latency, and the failure modes you don't see yet.
Models are five percent of the system. The rest is data, queues, GPUs, caches, evals, fallbacks, and the on-call playbook. We build the infrastructure layer so the model is the thing your team gets to think about — not the thing that wakes them up.
Why infra is its own discipline now
The cost of a clever model decision and the cost of a careless infra decision are about an order of magnitude apart, in opposite directions. A 30% inference cost reduction at scale is worth more than a 2-point eval improvement. We treat infra as a first-class engineering practice with its own metrics, its own roadmap, and its own on-call.
What we operate
Training platform
Spot-friendly clusters, checkpointing, experiment tracking, reproducible runs.
Inference fleet
Multi-region routing, autoscaling, KV-cache reuse, fallback policies across providers.
Eval harness
Offline benchmarks, LLM-as-judge, online A/B, drift detection — one dashboard.
Data lake
Versioned training data, prompt logs, feedback loops, regulatory retention.
Observability
Traces, token accounting, latency budgets, per-customer cost attribution.
Security posture
Secrets, PII redaction, regional egress, audit trails, SOC 2 readiness.
How we engage
- 01
Audit
Two-week read of your current stack, costs, evals, and incident history.
- 02
Harden
Fix the load-bearing brittleness — runaway costs, missing fallbacks, eval blind spots.
- 03
Automate
Replace the runbooks with controllers. Replace the heroes with on-call rotations.
- 04
Handoff
Document, train, and stand back. Optional Run-and-Maintain engagement keeps us on call.