How we work
Assess first. Build the foundation. Stay to run it.
Every engagement follows the same shape: a short assessment that says what to build first, a build designed to show a measurable result within ninety days, and an operating model that keeps the system working after the project ends. We build, deploy and operate. We do not hand over a strategy document and leave.
The Selberg model
Data becomes a decision in five steps.
Most companies are stuck between step one and step two: data that is scattered, untrusted and unused. Everything above that is built on sand. We start where you actually are and move up the steps in order.
01
Data
Every system you already run, connected.
02
Trusted information
One agreed version, traceable to its source.
03
Analytics
What is happening, and why.
04
Intelligence
What is likely to happen next.
05
Automated action
Work done by the system, exceptions to people.
Where every engagement starts
The assessment
Three weeks. We look at your systems, your data and your questions, and we tell you exactly what we would build first, what it depends on, and what result you should expect within ninety days. It is a real deliverable, not a sales call, and you own it whatever you decide next.
What you receive
- A plain account of the current state: where the data is, what is trusted, what is not
- The target architecture, drawn, with the technology choices and why
- The first ninety-day plan: what gets built, in what order, with the result it produces
- Governance, security and operating-model requirements the plan depends on
- A fixed scope and price for the first build
What we need from you
One business question that matters. Access to two or three of your systems. One sponsor on your side.
The engagement lifecycle
Six stages, one team, no hand-off.
01 · Assess
Understand the environment and set priorities.
Data maturity, platform, architecture, AI readiness, governance and data-quality assessments.
02 · Architect
Design the target state and the roadmap.
Target architecture, data and AI strategy, technology recommendations, governance framework, solution architecture.
03 · Build
Engineer and deploy production capabilities.
Data platforms, pipelines, warehouses and lakehouses, analytics, machine-learning systems, AI applications, enterprise agents.
04 · Modernize
Move legacy environments to modern platforms.
Cloud migration, warehouse and pipeline modernisation, architecture redesign, integration and analytics modernisation.
05 · Operate
Run what we build, as a managed service.
Pipeline and data-quality monitoring, platform administration, incident response, cost and performance tuning, MLOps and agent monitoring, a named architect.
06 · Expand
Build the next thing on the same foundation.
New questions, new automation and new AI without rebuilding the platform underneath them.
How we decide
Six principles that shape every recommendation.
Engineering first
We design systems that can actually be built, operated, monitored, governed and maintained.
Data before AI
Reliable AI requires reliable data. The foundation comes first.
Business value over technology fashion
We prioritise use cases that improve revenue, efficiency, decisions, risk or customer experience.
Production over demonstration
A successful proof of concept is not the objective. A system running reliably at scale is.
Governance by design
Security, quality, lineage, access, observability and accountability are designed in from the beginning.
Architecture for scale
Platforms you build on for years, instead of foundations you rebuild every time.
Neutral on tools
We work across Microsoft Azure and Fabric, Power BI, Databricks, Snowflake, AWS and Google Cloud, with Python, SQL, Spark, dbt, Airflow and Kafka, and with the leading AI models from OpenAI and Anthropic. Our role is not to sell a platform. It is to choose the combination that fits your requirements, your environment, your security needs and your strategy.