We build data platforms, pipelines, and analytics products that make information usable — governed, observable, and fast enough to power real-time decisions.
Who this is for
- Companies whose reporting still runs on spreadsheets pulled by hand from five different systems.
- Teams whose analytics queries get slower every quarter as data volume grows.
- Businesses that need real-time or near-real-time decisions (fraud, logistics, energy load) instead of yesterday’s batch report.
- Organizations that need to trust their numbers — governance, lineage, and a single definition of “revenue” across teams.
Problems we solve
The challenge
Every team has its own spreadsheet version of “the truth,” and none of them agree.
Our approach
We build a governed data platform with a single source of truth, ownership, and data contracts, so “active users” means the same thing in every dashboard.
The challenge
The data platform was sized for last year’s volume, and queries get slower every month.
Our approach
We design for the scale you’ll hit, not the scale you have — the same boundary-setting approach from our own reference architecture for data platforms that scale.
The challenge
Decisions that need to happen in minutes are running on a report that updates once a day.
Our approach
We build streaming pipelines — the same pattern behind a platform we built ingesting 400k smart-meter feeds at 5-minute forecast granularity — so the business reacts in near-real time.
The challenge
Nobody trusts the dashboard, so decisions get made on gut feel instead.
Our approach
We put data quality and lineage in the pipeline itself — every number traceable back to its source — so BI stops being a debate and starts being an answer.
Capabilities
- Data platforms & lakehouses
- Streaming & real-time analytics
- Data governance & quality
- BI & decision intelligence
How we work
- 01
Map the sources
Inventory where data actually lives today, who owns it, and where the current model breaks down.
- 02
Design the platform
Storage layout, table formats, and streaming-versus-batch boundaries sized for where you’ll be in two years, not just today.
- 03
Build the pipelines
Ingestion, transformation, and governance as code, with quality checks built in rather than discovered downstream.
- 04
Enable the teams
BI, self-serve analytics, and documentation so analysts and product teams can move without filing a ticket.
Technologies we work with
- Snowflake
- Databricks
- Apache Spark
- Apache Kafka
- dbt
- Airflow
- AWS Redshift
- BigQuery
- Looker
- Apache Iceberg
In numbers
Frequently asked questions
We’re a small team — is a “data platform” overkill for us?
Not if it’s scoped right. We start with the two or three decisions your business actually needs data to answer, and build only the pipeline needed to answer them well — the platform grows from there.
Do you work with our existing warehouse (Snowflake, BigQuery, Redshift), or do we have to switch?
We work with what you have wherever it’s a reasonable fit; a migration is only on the table when the current platform is genuinely the bottleneck, not by default.
How is a “data engineering company” different from hiring a BI consultant?
A BI consultant makes dashboards faster to build on what exists; we also build and govern the pipelines and platform underneath, so the dashboards stay fast and correct as volume grows.
Can you help with real-time / streaming use cases specifically, not just batch reporting?
Yes — streaming and real-time analytics is one of our core capabilities, from telemetry ingestion to sub-5-minute forecasting pipelines.
What does “data governance” actually mean in a project like this?
Ownership, lineage, and access control built into the pipeline — so you can say who owns a metric, where a number came from, and who’s allowed to see it, without a manual audit.