Enterprise Architecture & Agentic AI
Enterprise architecture, process intelligence, and agentic AI — engineered as one discipline.
Most AI pilots stall in production because teams skip straight to "building agents." We start where the outcome is actually decided: understand the enterprise, simplify the process, then match the architecture to the use case.
Why together
Three sequential unlocks, not one model deployment
A successful AI transformation is technical readiness, then organizational and process readiness, and only then an architecture choice matched to the use case. Skip the first two and the pilot never reaches production.
1. Enterprise Architecture
Clean data, documented interfaces, and a technology roadmap the rest of the organisation can build on — the technical readiness an agent's tool calls quietly depend on.
2. Business Process Analysis
Explicit decision rules, named exception paths, and a human checkpoint before every consequential action — the process readiness that turns tribal knowledge into something an agent can actually run.
3. AI Transformation
The foundation, orchestration pattern, and harness matched to each specific use case — chosen deliberately, not defaulted to the most autonomous option available.
What we do
Three competencies, one delivery team
Enterprise Architecture
We map the systems, data flows, and integration points AI has to call as tools — and fix what's brittle before it breaks an agent silently.
Read more →Business Process Analysis & Optimization
We turn tribal knowledge into documented, versioned procedures with explicit decision rules and approval gates — the raw material every agentic workflow runs on.
Read more →AI Transformation
From discovery workshops to a governed, production agentic architecture — foundation, orchestration, guardrails, and the harness to run it.
Read more →Free download
The AI Transformation Blueprint
Readiness prerequisites, the building blocks of a production agentic architecture, EU AI Act & GDPR compliance mapping, security threats and countermeasures, and a phased roadmap.
Get the free blueprintFrom the masterclass
Notes on building agentic AI
Case Study: Automating a Job Search With an Agentic Workflow
A fork-and-own, Claude-native framework for job applications, built entirely differently from the TradingAgents case study — the contrast between the two is itself the lesson.
Case Study: How a Multi-Agent System Debates a Stock Trade
TradingAgents is a real, open-source multi-agent trading system — analyst team, structured bull-vs-bear debate, independent risk gatekeeping, and a five-tier decision — where nearly every idea in this series shows up in working code.
Data Sovereignty in the Age of Agents: What "Zero Data Retention" Really Means
"Zero data retention" gets used almost as often as a marketing phrase as a precise technical commitment. It helps to break "data sovereignty" into four distinct, concrete levers rather than one vague checkbox.