Connectors for industrial protocols, enterprise systems, partner APIs, and legacy data dumps.
Capability
Data Integration & Operational Data Platforms
Operational data is messy, distributed, and contested. We build the platforms that turn fragmented sources — historians, ERPs, sensors, spreadsheets, partner feeds — into one decision-grade system of record. Every pipeline is observable, every transformation is owned, every consumer is named.
Components
What we build.
Each engagement assembles a different combination of these components, but the parts are stable.
Lakehouse architectures with governance baked in. Iceberg or Delta. Versioned, queryable, replayable.
dbt-style modelling with operational semantics. Lineage from raw signal to executive metric.
APIs, dashboards, and operator-facing apps consuming the same governed layer.
Stack
Tech we deploy with.
The list is descriptive, not prescriptive — the stack meets the operation.
PostgreSQL ClickHouse Kafka Iceberg dbt Airflow Trino Python
Detail
How it deploys.
We start at the messiest source, not the cleanest. The first pipeline is usually the one everyone avoids — the undocumented historian, the ERP export that breaks weekly. We get one real feed observable and in production before widening the surface.
The platform grows by consumer, not by table. Every dataset we land has a named owner and a named consumer — a dashboard, a model, an operator app. Nothing is ingested “for later.”
We hand over a system that stays legible. Lineage from raw signal to executive metric, every transformation owned, every pipeline replayable. When we leave, your team can still reason about it.
Domains
Where we apply this.
Engage
Start a project around data platforms.
Tell us the operational gap. We'll respond with the shape of the engagement within one business day.
Other capabilities