What we do·AI & Data·Data
Data & Intelligence.
Warehouses and pipelines your CFO stops double-checking.
Data platforms fail socially before they fail technically: nobody trusts the number. We build the pipeline and the trust. Tested dbt graphs, documented metrics, and lineage you can show an auditor.
1.5M+
Service locations reporting through one client platform
10+ yrs
Same data platform, inception → IPO
How we approach it
Warehouse architecture (Snowflake, BigQuery, Databricks), dbt semantic layers, streaming ingestion (Kafka), orchestration (Airflow / Dagster), BI enablement, and cost control as a standing agenda item.
Warehouse architecture (Snowflake, BigQuery, Databricks), dbt semantic layers, streaming ingestion (Kafka), orchestration (Airflow / Dagster), BI enablement, and cost control as a standing agenda item.
- A metrics layer finance, product, and sales all sign
- Pipelines that alert before the dashboard lies
- Warehouse spend typically cut 30–50% in quarter one
- ML-ready: feature stores and training data on the same rails
Proof
We've shipped this before.
FAQ
Before you ask.
Can you fix trust in our existing dashboards?
That’s the usual brief. We audit lineage, add tests, and re-launch the five numbers that matter with documented definitions.
How big does our data have to be?
Our largest client’s platform reports across 1.5M+ service locations; most clients are far smaller. The discipline (tested pipelines, documented definitions) pays at every size.
How we approach it
Warehouse architecture (Snowflake, BigQuery, Databricks), dbt semantic layers, streaming ingestion (Kafka), orchestration (Airflow / Dagster), BI enablement, and cost control as a standing agenda item.
- Service locations reporting through one client platform
- 1.5M+
- Same data platform, inception → IPO
- 10+ yrs
Proof
We've shipped this before.
FAQ
Before you ask.
Can you fix trust in our existing dashboards?
That’s the usual brief. We audit lineage, add tests, and re-launch the five numbers that matter with documented definitions.
How big does our data have to be?
Our largest client’s platform reports across 1.5M+ service locations; most clients are far smaller. The discipline (tested pipelines, documented definitions) pays at every size.
Tell us the hard part.
A 30-minute call with an engineer, not a salesperson. Honest scoping, real dates.