Crayon Data

Enterprise data and engineering leadership.

The data preparation, inference and retrieval around a billion-node graph mattered as much as the model.

Ground truth comes first.

Build the data system around the model

I founded Avagam and continued the work after its acquisition into Crayon Data. As Technology Leader, reporting to the CEO, I led a 35+ person engineering organisation working on graph and machine-learning capabilities.

The billion-node graph made the surrounding engineering visible. Data processing, inference and retrieval determined whether a model could become a usable product. The model was one part of the system we had to build and operate.

The platform offered multi-tenant SaaS and dedicated single-tenant cloud deployments. Those options addressed different enterprise data requirements while keeping the delivery model in the cloud. At Dragon Wealth, the same question became more specific: how to turn financial documents into facts a recommendation could use.

Evidence and outcomes

Crayon Data was a CODE_n top-50 finalist at CeBIT in March 2014. The platform was later recognised as a TiE50 winner in May 2014, after I left in April.

Dragon Wealth: from fund documents to portfolios