About Us

After 25+ years working in financial markets, building risk models, and designing data platforms, one problem kept resurfacing — complex systems become opaque faster than teams can document or reason about them.

That led to a shift toward knowledge graphs and GraphRAG workflows — building deterministic, queryable models of code and data that can be used by both engineers and AI systems.

Along the way, this approach has been applied to:

• financial risk modelling and decision systems
• enterprise data platforms and system migrations
• large-scale codebase analysis using graph-based models
• GraphRAG workflows that constrain and validate LLM outputs

Today, the focus is on helping organisations build Codebase Knowledge Graphs — enabling safer refactoring, better system understanding, and more reliable AI-assisted development.

The goal is simple:

To turn complex, opaque systems into structured, explainable, and queryable assets.