MLOps & AI Platform Engineering
The infrastructure under the model: serving, scaling, caching, fallbacks, evaluation in CI, and the cost and quality observability that decides whether an AI feature survives contact with real traffic.
- Inference serving
- Semantic caching
- Observability
- Cost control
The demo runs on one laptop; production runs on your bill
AI systems fail in production for unglamorous reasons — a provider outage with no fallback, a prompt change nobody evaluated, a cost curve nobody watched until the invoice. We build the platform layer that makes those failure modes visible and recoverable.
Capabilities in this practice
Each of these ships as a working system integrated with what you already run — not a slide deck or a proof of concept that stalls at the pilot.
Inference Infrastructure
Self-hosted serving with vLLM or managed endpoints on Bedrock, Vertex and SageMaker, with autoscaling sized to real traffic patterns rather than peak guesswork.
Caching & Routing
Exact and semantic caching, prompt compression and tiered routing that sends easy requests to small models and escalates only what needs the frontier tier.
Reliability & Fallbacks
Multi-provider failover, timeouts, retries with backoff and graceful degradation, so one vendor incident is not one of your outages.
Evaluation in CI/CD
Prompts, models and datasets versioned as artefacts, with eval suites running on every change and regressions blocking the merge.
Observability
Traces across the full chain — retrieval, tool calls, generation — with token, latency and spend attribution per feature, tenant and user.
Governance & Security
Prompt-injection defence, PII redaction, audit trails, tenant isolation and data-residency controls suitable for regulated environments.
A sequence built to de-risk, not to impress
We measure before we optimise and ship in slices, so you can stop, redirect or scale at any step with evidence rather than instinct.
- 1
Baseline
Instrument what exists — latency, cost per request, failure rate and quality — before optimising anything.
- 2
Platform design
Choose serving topology, caching strategy and routing tiers against the measured traffic profile.
- 3
Eval pipeline
Wire evaluation into CI so quality becomes a build gate rather than a post-incident discovery.
- 4
Harden
Fallbacks, rate limits, circuit breakers, spend caps and tenant isolation.
- 5
Observe
Dashboards and alerts on drift, cost anomalies, error clusters and low-confidence output.
- 6
Optimise continuously
Right-size models and tune caching as usage patterns move; unit costs should fall as volume grows.
Platform work behind our own products
We build our own products on this stack. When we recommend an approach, it is one we already run in production and pay the bills for.
The things clients ask before signing
Managed APIs win on time-to-market and on access to frontier capability. Self-hosting wins on unit cost at sustained volume and on data residency. Most of the systems we build use both — a managed frontier model for hard requests, a self-hosted smaller model for the high-volume path.
It depends entirely on request diversity. Workloads with repeated or near-duplicate queries see large reductions from semantic caching; highly varied workloads gain more from tiered routing and model right-sizing. We measure your traffic distribution before promising a number.
Yes. We build on AWS, Azure and GCP with Terraform, Kubernetes and your existing CI, rather than introducing a parallel stack your team then has to learn and maintain.
Ready to put this into production?
Tell us the problem you are trying to solve. We will tell you honestly whether AI is the right tool for it, and what it would take to ship.