MLOps & Model Infrastructure
Models drift, data shifts and prompts rot. Without measurement you find out from a customer complaint. We build the infrastructure that tells you first.
What this includes
Evaluation pipelines
Automated scoring against golden datasets on every change, wired into CI.
Observability
Token usage, latency percentiles, retrieval hit rates and failure modes on one dashboard.
Model serving
Autoscaling inference with GPU scheduling and request batching.
Experiment tracking
Versioned prompts, configs and datasets so any result can be reproduced.
Drift detection
Alerts when input distributions or output quality move away from baseline.
Deployment automation
Blue-green and canary rollouts for models and prompts alike.
What you end up with
- Quality regressions caught in CI, not production
- Reproducible experiments
- Inference cost visible per feature and tenant
- Safe, reversible model rollouts
Tools we reach for
Frequently asked
We have no evaluation set. Where do we start?
With roughly fifty real questions and their correct answers. That is enough to catch most regressions, and it grows naturally from production traffic.
Is this worth it before launch?
Yes — it is cheapest to build before you have traffic, and it is what lets you ship changes confidently afterwards.
Other work in this practice
RAG & Knowledge Systems
Turn scattered documents into a knowledge layer your AI can answer from — accurately, with sources.
Read moreAI Agents & Automation
Agents that use real tools, take real actions, and know their limits.
Read moreLLM Integration
Add language model capability to an existing product without destabilising it.
Read moreLet's talk about what you're building
Tell us the problem. We'll tell you honestly whether AI is the right tool, and what it would take.