Service
Semantic Architecture
Design data architectures that speak your organization's language and accelerate delivery with pre-built starter kits.
The language problem
Even within the same industry, every company features a distinct language that has evolved from its unique history, leadership, and diverse business mix. One recruiting company says "candidates." Another says "talent." But both use the same applicant tracking system with its own, different, language.
Data teams are forced to translate by building an ad-hoc semantic layer, a data architecture that speaks the company's language and drives its reporting, analytics, and data science initiatives. This improves the experience of consuming data but slows development as the model grows, and the language shifts over time force constant rework.
Our approach
Instead of spending a year progressively building out a semantic layer, our starter kits allow a data team to spend roughly a month reshaping existing raw data. After that, the entire reporting suite just works.
Each starter kit includes:
- A fully-realized medallion architecture with bronze, silver, and gold layers
- Physical and virtual transformations between each layer
- Observability and testing scripts
- Reporting and dashboard templates
- Detailed data catalog metadata with full lineage
- A data model optimized for LLM natural language queries
Engagement options
Technology-agnostic
Our process works across data platforms, though platforms with broader feature sets like MS Fabric, Databricks, and Snowflake are the easiest to package, deploy, and maintain.