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Your AI Is Only as Good as the Data Infrastructure Behind It
I advise and architect the data infrastructure layer that makes enterprise AI reliable, scalable, and trustworthy. From feature engineering pipelines and vector databases to real-time inference data feeds and AI-ready CRM integrations — I design the foundation your AI models actually need to work.
I data infrastructure is the complete set of data systems, pipelines, storage layers, and integration frameworks that feed, train, evaluate, and serve your AI and machine learning models. It is the difference between an AI proof-of-concept that works in a notebook and an AI capability that works reliably in production at enterprise scale.
AI architecture advisory goes beyond selecting tools. It means designing a coherent, layered data system where raw data is ingested cleanly, transformed reliably, stored efficiently, and served consistently to both training jobs and live inference endpoints — without breaking every time the upstream CRM or operational system changes.
Designing end-to-end AI data platforms on Azure, AWS, and GCP — covering ingestion, transformation, storage, feature engineering, and model serving layers — built for production reliability and enterprise scale.
Building reliable, monitored, and versioned data pipelines for machine learning — covering data ingestion, transformation, validation, labelling workflows, and automated quality checks that keep training data trustworthy.
Architecting feature stores that ensure consistency between training and inference environments — eliminating training-serving skew, enabling feature reuse, and accelerating time-to-production for ML models.
Connecting your CRM platforms, master data management systems, and operational databases into a unified AI-ready data layer — ensuring your models are trained and served on governed, accurate customer data.
Designing vector database infrastructure for Retrieval-Augmented Generation (RAG) applications, semantic search, and LLM-powered workflows — integrated with your CRM and enterprise customer data.
dvising on the data infrastructure components of your MLOps practice — model registry integration, feature pipeline monitoring, data drift detection, and the governance frameworks that keep deployed models accountable.
We evaluate your current data environment against the requirements of your AI use cases — assessing data quality, pipeline reliability, governance maturity, platform capabilities, and the gaps standing between you and production-ready AI.
I design the target-state AI data infrastructure tailored to your cloud platforms, existing data assets, and AI goals — with a phased roadmap that prioritises the highest-value capabilities first and builds toward a scalable, governed AI data foundation.
I support delivery of the architecture — advising on platform configuration, pipeline design, feature store implementation, and integration with CRM and MDM systems — and enabling your data and ML engineering teams to own it in production.
I advise on AI data platforms from the perspective of someone who has spent 15 years making enterprise data trustworthy — which is exactly what AI models need to be reliable in production.
CRM architecture, AI data strategy, and MDM
Practical, experience-driven thinking on enterprise CRM architecture, AI in customer data, MDM strategy, and data governance — written for practitioners and decision-makers alike.