A software-architecture practice for the AI era
Systems your team can
build, run and own
Three decades of production-system experience, applied to the hard decisions AI now brings.
Getting AI to work in a demo is the easy part; the hard part is everything after.
Through workshops and principal-led consulting, we help teams close that gap – and build the capability to handle what comes next.
AI keeps getting more powerful. Accountability stays human.
Consulting and advisory
Have a problem to solve? We work alongside you on a live system, architecture, or decision. We’ll start with the smallest useful engagement.
Production-Ready AI
When a live system needs senior intervention.
A stalled PoC, a difficult architecture or security decision, or a system that needs to cross the gap between convincing demo and dependable production. We diagnose, architect, build where needed, and hand the capability back to your team.
Architecture Advisory
When the hard decisions keep coming.
Regular senior support for architects, engineering leads and technical leaders carrying calls they will have to live with. Bring the live decision; leave with the reasoning, design and decision record.
Workshops and briefings
When the capability needs to live inside your people. Practice-based: your team works on its own systems, code, and decisions – the person, the team, and the project.
The project
Agentic AI for Products & Systems
For technical leaders deciding whether an agent belongs at all.
Agent demo → production decision
The team
AI in the SDLC
For teams adopting AI across delivery without giving away control.
Faster team → accountable team
The person
Software Architecture: Principles, Patterns & Practice
For senior engineers taking on architectural responsibility.
Senior engineer → architect
Examples of problem solving
Three examples from real engagements where the obvious answer was not the right one.
- Don’t automate the human who still owns the decision. In legal document review, the architecture was designed around the lawyer’s review workflow rather than pretending autonomous review was the goal. Read the field note →
- Don’t build the feature if the reusable capability is the real asset. A sales assistant became a shared knowledge engine supporting multiple products rather than another isolated chatbot. Read the field note →
- Don’t vectorize the warehouse just because RAG looks like the easy answer. For an enterprise analytics assistant, we rejected a proposal to embed the entire warehouse and instead used schema-aware SQL generation, splitting exploratory questions from must-be-right ones. Read the field note →
Recent work includes production systems for Fortune 500 and FTSE-100 companies.
See the case studies →Read the field notes →Our approach
Tools change quickly. The hard questions don’t. Across consulting, advisory and workshops, we use the same five-step discipline:
Frame → Options → Size → Decide → Record
The detail changes with the problem. The principle does not: match the depth of reasoning and verification to what is at stake. See details of our approach →
Facing a hard architectural decision?
Send us the problem. You'll get an honest assessment.
Request an assessment