Data and AI engineered around your outcomes.
In an AI-driven market, fragmented data estates are becoming a competitive liability. But the fragmentation that costs most is the distance between the people who understand the business problem and the people building the solution. I close both gaps.
How I work differently
Most transformations track capability. The ones that succeed start with outcomes.
Outcomes before architecture
Every engagement starts by naming the business outcome — speed, risk reduction, revenue, or operating efficiency. The technology exists to serve that outcome. Not the other way around.
No gap between boardroom and engineering
I lead the team and write the code. Nothing gets lost between what leadership is accountable for and what engineering ships. The same person in your strategy session is accountable for what goes into production.
AI in the platform from day one
AI retrofitted onto fragmented data rarely works at scale. I architect platforms where AI is part of the foundation — built to act on insights, not just surface them. In production. Not in a pilot.
You own what we build
You get a specialist team without the overhead of recruiting or managing them. When the engagement ends, you own a governed, AI-ready platform your people can run, evolve, and build on.
What I build
The engineering serves the outcome.
Data Strategy & Architecture
Your organisation has data in dozens of systems. But no one trusts the numbers, reports take days, and every new initiative starts with 'first we need to fix the data.' I design the architecture that makes all of that disappear.
- Data platform assessment and roadmap
- Cloud architecture design (Azure-native)
- Data governance framework and Purview setup
- Technology stack selection and migration path
Data Engineering & Pipelines
You have the strategy. But pipelines break overnight, data arrives late, and your analysts spend more time cleaning than analysing. I build the engineering layer that makes data reliable, fast, and self-healing.
- ETL/ELT pipeline design and implementation
- Delta Lake and lakehouse architecture
- Real-time streaming data processing
- Data quality, monitoring, and alerting frameworks
ML & AI Engineering
You have data. You have ideas for AI. But models sit in notebooks, never reaching production, and GenAI pilots stall after the demo. I take ML from experiment to production system with full MLOps discipline.
- ML model development and optimisation
- Azure OpenAI, RAG, and LLM integration
- MLOps CI/CD pipeline implementation
- Model monitoring, governance, and responsible AI
Client outcomes
Business problems solved with data and AI - the platform was the means, these are the results.
Enterprise Data Warehouse for Healthcare Leader
Data Architecture
A leading consumer healthcare company faced data fragmentation across 100+ sources, resulting in inefficient reporting, slow decision-making, and high...
Unified Cybersecurity Intelligence Platform
Security Analytics
A major pharmaceutical company lacked a single source of truth for cybersecurity assets, hampering consistent reporting, risk assessment, and overall ...
Strategic Cloud Migration Intelligence Engine
Infrastructure Optimization
Global pharmaceutical company's cloud migration stalled due to complex, hidden IT dependencies. Decommissioning servers based solely on utilization ri...
Start with the outcome. Everything else follows.
A 30-minute conversation to map the gap between where your data platform is and what your business decisions actually need. Walk away with clarity on where to focus — whether you engage me or not.