Five disciplines, usually delivered together
From the source system through the warehouse to the application a real user clicks on — and the ML and AI workflows we layer on top where they actually move the business. Most of our engagements touch more than one of these, and that overlap is where the interesting bugs live.
What we cover
We are deliberately narrow. These are the five areas where we go deep — and a project usually involves at least two of them.
Data Analytics
Analytics platforms that survive the gap between dashboard demo and finance audit. Snowflake or BigQuery, DBT for the modelling layer, BI tools chosen for the team that has to live with them.
- Real-estate big data
- Self-service BI
- Reconcilable KPIs
- Pipeline observability
Big Data Warehousing
The unglamorous work of getting one set of numbers out of many systems. Snowflake or BigQuery, schema discipline, and cost control as a first-class concern.
- Snowflake & BigQuery
- Cost control
- Schema governance
- Automated ingestion
Data Engineering
ELT and streaming pipelines built to keep running on their own. Airflow or Dagster for orchestration, DBT for transformation, plus the boring monitoring no one demos.
- Airflow & Dagster
- ELT/ETL pipelines
- Stream processing
- HubSpot ↔ PostgreSQL sync
Enterprise SaaS
Custom SaaS on Google Cloud — Next.js, NestJS, Python, Go. The interesting projects always need to reach beyond the browser: CRM, billing, or hardware on a factory floor.
- GCP-native
- API-first
- GDPR by design
- Production-floor integration
AI & ML
ML for the parts of the business that are predictable enough to learn — and AI agents for the parts that aren't, with a Human in the Loop (HITL) at the confidence cliff.
- Lead scoring & price models
- HITL document processing
- Research agents for real estate
- Automated scheduling
Have a project in mind?
Tell us about it — we would love to hear what you are working on, and we will get back to you personally.