Services
AI & MLOps consulting across two tracks: hands-on architecture and engineering of ML platforms, pipelines, and Generative AI services; and strategic advisory on Data & AI direction, capability, and roadmap. Pragmatic, outcome-focused, and grounded in production experience.
Delivered hands-on, from first principles to production.
Developing Data & AI strategies that are grounded in organisational reality — assessing current capabilities, defining a roadmap, aligning stakeholders, and translating ambition into a workable plan. Delivered as a structured engagement or embedded alongside a technical project.
Architecture and implementation of ML platforms on Azure ML and GCP Vertex AI — model registries, training pipelines, and serving infrastructure built to scale. Terraform-based IaC and RBAC from day one.
Containerised, reproducible ML pipelines with automated training, evaluation, and deployment. CI/CD templates with Azure DevOps, GitHub Actions, or Cloud Build — so your data science team can ship models without a release bottleneck.
Drift detection, data quality checks, and alerting so you know when models degrade before your users do. MLflow-based experiment tracking and model registry to keep the full model lifecycle under control.
Cloud-native delivery on Microsoft Azure and Google Cloud Platform — networking, storage, identity, and access provisioned with Terraform and aligned with enterprise security requirements. On-premise deployments also supported.
Pragmatic LLM integration — RAG pipelines with OpenAI and Qdrant, document processing APIs with Azure OpenAI, prompt engineering, and evaluation frameworks. Designed for enterprise workflows, not just demos.
Training, workshops, and coaching that help data science teams adopt MLOps best practices and own their production systems with confidence. Delivered as structured workshops or embedded alongside a project.
Engagements are scoped to your situation — no fixed packages.
Embedded in your team as a senior engineer or architect — building the platform, writing the code, and shipping models alongside your data scientists.
A structured engagement to define your Data & AI direction — assessing current capabilities, identifying priorities, and producing a clear roadmap your leadership and technical teams can act on.
A focused deep dive into your existing setup — identifying gaps, risks, and quick wins, with actionable recommendations for where to go next.
Hands-on sessions on Generative AI, MLOps best practices, and platform tooling — designed to accelerate adoption across technical and non-technical teams alike.