MLOps & AI Infrastructure services

We package it with its dependencies and preprocessing, wrap it in a versioned API or batch job, add tests and monitoring, and deploy it through a pipeline so the next version ships the same way. Most single-model deployments take two to four weeks including monitoring.

Latency, error rates and cost per request as with any service, plus AI-specific signals: output quality against an evaluation set, hallucination and refusal rates, prompt-injection attempts, drift in inputs, and per-tenant token usage. Traces of prompts and tool calls make debugging possible.

It varies, but common wins are large: caching repeated prompts, routing simple requests to smaller models, batching offline work, spot or reserved GPUs and right-sizing instances. We baseline spend first and report savings against it rather than promising a percentage.

Yes. We build on AWS, Azure and GCP with the tools you already run (Kubernetes, Terraform, GitHub Actions, MLflow, SageMaker, Vertex) and only introduce new components when there is a clear gap. Where you have no platform yet, we recommend a small, standard stack (Kubernetes or managed serving, MLflow, an observability tool) that your team can hire for.

Least-privilege tools, input and output filtering, separating instructions from untrusted content, human approval for sensitive actions, and regular red-teaming. No single control is sufficient, so we layer them and log everything. We test defences with realistic attack prompts as part of release, and re-test whenever tools or models change, since new capabilities open new paths.

A registry of models and prompts with owners and versions, documented evaluations, approval steps for production changes, audit logs of decisions and data use, and policies for acceptable use. We keep it lightweight enough that teams actually follow it. The registry doubles as documentation, so when an auditor or customer asks how a model was built and tested, the answer already exists.