Service 02
Architecture & Engineering
From target architecture to production. Data & AI platforms, agents, ML models and automation - spec-driven, reusable and built to last.
Why this matters
Strategy only creates value once something runs in production. That last stretch - from a promising prototype to a reliable system that is integrated, monitored and maintained - is where most AI initiatives stall. Demos are quick to build; production systems are not.
This service closes that gap. I design the data and AI architecture these systems run on, and I build AI agents, machine learning models and automation with the same discipline as any other business-critical software: clear specifications, reusable components, tests, monitoring and a handover your teams can own. The goal is not a showcase but a capability that keeps delivering after the first version - and can evolve as models and requirements change.
Data & AI architecture
A cloud-native target architecture for data and AI - from ingestion and streaming to warehouse, lakehouse and model serving - derived from your requirements, not from a vendor slide.
Why it matters
Every AI initiative eventually hits the same wall: the data isn’t where it needs to be, in the shape it needs to be, when it needs to be there. Overnight batch jobs can’t feed real-time decisions, on-premise systems can’t keep up with demand, and a patchwork of tools makes governance close to impossible.
Architecture work starts with requirements, not technology: latency, volumes, data sensitivity, existing systems, team skills and budget. From there we design the target picture - how data is ingested, streamed, stored, governed and served to analytics, ML models and agents - and the path to get there, including the migration from legacy and on-premise systems.
Critical assumptions are tested early with a focused proof of concept instead of discovered in production. The result is a platform the next ten use cases can build on, with running costs that are transparent from the start - and one that can adopt new services and models without being redesigned every time.
AI agents
Agents that work with your tools, data and processes - built to be reliable, observable and maintainable.
Why it matters
Agents move AI from answering to acting: they retrieve information, call systems, draft and check outputs, and hand over to people when needed. In the right workflows they take over work that used to require many manual steps across several tools.
But agents are also easy to build badly. A prototype that works in a demo can behave unpredictably with real data, run up costs or take actions nobody can trace afterwards. Without evaluation, monitoring and clear permissions, trust erodes quickly - and the project gets shelved.
I build agents spec-first: defined goals and boundaries, evaluation against realistic test cases, observability for every step and least-privilege access to your systems. Agents are assembled from reusable components - tools, prompts, retrieval, guardrails - so the next agent is faster and cheaper to deliver, and switching to a better model later does not mean starting over.
Machine learning
Predictive models that improve concrete decisions - from forecasting to churn and pricing - running reliably in production.
Why it matters
Not every problem needs a large language model. Many of the most valuable decisions in a company - how much to stock, which customers are about to leave, how to price, where to intervene - are best served by classic machine learning on your own data. These models are often cheaper to run, easier to explain and directly tied to a measurable business outcome.
The hard part is rarely the algorithm. It is reliable data pipelines, features that are consistent between training and production, integration into the systems where decisions are actually made, and monitoring that notices when the world changes and a model starts to drift.
That last point matters more than ever. Customer behaviour, markets and supply chains shift quickly; a model trained on last year’s reality slowly becomes wrong. Building models with MLOps practices - automated retraining, performance monitoring and versioning - keeps them valuable instead of letting them decay silently.
Automation
Automating repetitive processes and data flows - classic automation where it is enough, AI where it adds value.
Why it matters
A large share of everyday work is still manual: copying data between systems, checking documents, compiling reports, routing requests. Each of these steps costs time, introduces errors and slows the organisation down - and it is exactly the work people least want to do.
Automation frees that capacity. The key is choosing the right tool for each step: deterministic workflows and integrations where rules are clear, AI where inputs are unstructured - emails, documents, free text - and human review where judgement is needed. Combining these deliberately produces robust processes rather than brittle scripts.
In a fast-changing environment, automation is also about agility. Processes built on clean integrations and documented workflows can be adapted in days when regulations, products or systems change. Automation hidden in undocumented macros and personal scripts does the opposite - it locks the organisation into yesterday’s way of working.
How we work on it
Formats for this service
Hands-on delivery into production - agents, ML and automation that keep running after the first version.
Let's talk about your situation
In a free intro call we look at where you stand and which work package gives you the biggest leverage.
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