Single source of truth
One warehouse, one set of definitions, one place where the numbers come from. Stop arguing about whose dashboard is right.
Capability · Data
Data platforms that turn signal into decisions
From scattered operational data to a single source of truth — ingestion, modeling, dashboards, and predictive analytics, engineered to last.
What we ship
Modern data infrastructure, without the rebuild every two years
Data work is full of dead ends — fragile pipelines, dashboards nobody trusts, models that work on the test set and nowhere else. We've seen the failure patterns and we engineer around them.
- Ingestion from SaaS, databases, files, events, and IoT streams
- Warehousing on Snowflake, BigQuery, Redshift, or Databricks
- dbt-based modeling with tests, lineage, and documentation
- Dashboards in Looker, Metabase, Power BI, or custom React
- Reverse ETL to push insights back into operational tools
- Predictive analytics, forecasting, and anomaly detection
- Data governance, quality monitoring, and incident response
Data · BI
Snowflake · BigQuery · dbt · 
Airflow
Outcomes We Build For
Data work that pays for itself
Forecasting that holds up
Demand, churn, revenue, capacity — models with honest confidence intervals and explainable drivers, not magic numbers.
Real-time analytics
Streaming pipelines for events, telemetry, and operational data — with sub-minute latency where it matters.
Self-serve analytics
Modeled metrics, certified datasets, and friendly tools so business users get answers without filing a JIRA ticket.
Governance & trust
Lineage, access controls, PII handling, audit logs — engineered in from the start, not retrofitted in panic.
Embedded analytics
White-label dashboards inside your product. Customers get insights without ever leaving your app.
Process
How we build data platforms
Audit current state
What data exists, where it lives, who owns it, who breaks it. We map reality before changing it.
Define metrics layer
One canonical definition per metric, agreed across teams. The hardest meeting, done early.
Build pipelines
Ingestion, modeling, tests, docs — all version-controlled, all reviewable, all reproducible.
Deliver dashboards
Stakeholder-specific views, validated against real questions, instrumented for usage.
Operate & iterate
Monitoring, on-call, incident response, and continuous improvement against SLA.
Common Questions
What clients usually ask
Snowflake or BigQuery?
Either works. Snowflake tends to win on multi-cloud flexibility and ease of cost management. BigQuery wins if you're deep in GCP. We benchmark on your workload before deciding.
Do we need a data engineer in-house?
Eventually, yes — at scale. Early on, we run the platform and document everything so handoff is straightforward when you hire.
How fast can we get a usable dashboard?
A first vertical slice — one source, one model, one dashboard — typically lands in 3–4 weeks. Full platform builds run 3–6 months depending on scope.
What about ML?
We build forecasting, classification, anomaly detection, and recommendation systems on top of a solid data platform. Without the data foundation, ML is theatre — so we always start there.
Drowning in dashboards, starving for answers?
Tell us where your data hurts. We'll diagnose honestly and quote a realistic path forward.