Business Intelligence
Dashboards and reports built on governed metric definitions, so finance, sales and ops read the same numbers. Real-time visibility into the KPIs that run the business, not another spreadsheet export.
AI & Data
Data Analytics
One governed source of truth for your numbers: a modeled warehouse, tested pipelines and dashboards your teams actually use.
2-4 wksFrom kickoff to a first live dashboard on your data
100%Senior engineers, on every data engagement
1Accountable partner from architecture to handover
0Lock-in: you own the code, the models and the docs
What we build
Six capabilities, one stack: from raw sources to governed metrics, delivered by senior engineers on the cloud you already run.
Data Visualization
Charts that answer a question instead of decorating a slide. We design views around decisions: what changed, why, and what to do next.
Data Warehousing
A modeled warehouse on Snowflake, BigQuery or Databricks that unifies your sources into clean, documented layers. Built with dbt, versioned in git and tested on every run.
Real-Time Analytics
Streaming pipelines on Kafka or CDC feeds when the nightly batch is too late. Operational dashboards and alerts with latency measured in seconds, not hours.
Predictive Analytics
Churn, demand and scoring models trained on your warehouse data. Forecasts wired into the tools your teams already work in, not a notebook that never ships.
Data Governance
Access controls, lineage, metric definitions and audit trails built into the platform from day one. GDPR-ready by design, so compliance is a property of the pipeline, not a project.
What you walk away with
Every engagement produces artifacts your team owns and can run without us.
Data platform architecture
A written target architecture for your cloud: warehouse, ingestion, modeling and BI, with costs and trade-offs made explicit.
Modeled warehouse layers
Staging, core and mart layers built with dbt, versioned in git, documented and tested on every run.
Governed metric definitions
One agreed definition per metric, encoded in the semantic layer, so revenue means the same thing in every report.
Self-serve dashboards
Dashboards in Looker, Power BI or Metabase that answer the questions teams actually ask, without a ticket to the data team.
Data quality tests and alerting
Automated checks on freshness, volume and schema, with alerts that reach the right person before the CFO spots the gap.
Documentation and handover
Runbooks, lineage and onboarding docs, plus pairing sessions so your team operates the platform from the day we leave.
AI & Data
How an engagement runs
01
Assessment
We map your sources, pipelines and reporting, and interview the people who use the numbers. Two to three weeks in, you have an honest picture of what works, what is broken and what it costs.
02
Architecture Design
We design the target platform on your cloud: warehouse, ELT, modeling layers and BI. You review a written architecture with costs and trade-offs before we build anything.
03
Implementation
We build in vertical slices, so a first dashboard on live data ships in weeks. Every pipeline lands with tests, alerting and documentation, not as a black box.
04
Optimization
Once the platform runs, we tune query performance and warehouse spend, and extend to new domains. Your team takes over gradually, with pairing and handover built into the plan.
Three ways to engage
Fixed scope where it makes sense, embedded capacity where it does not. Every model starts with a free call with a senior engineer.
Data & analytics audit
2-3 weeks · fixed fee
A structured review of your data landscape that ends in a costed, prioritized roadmap. Useful on its own, and the safest first step with us.
- Inventory of sources, pipelines and reports
- Data quality and architecture assessment
- Target architecture for your cloud and stack
- Prioritized roadmap with effort and cost estimates
Analytics platform build
6-12 weeks · fixed scope
We design and build the platform end to end: ingestion, warehouse, modeling, dashboards and quality gates, then hand it over documented.
- Warehouse and ELT built on your cloud
- dbt models with tests and CI from day one
- Governed metrics and first self-serve dashboards
- Documentation, runbooks and team handover
Embedded data team
Monthly · ongoing
Senior data engineers working inside your team on your backlog. Capacity scales with the roadmap, knowledge stays in your company.
- Senior engineers embedded in your rituals and tools
- Roadmap delivery across platform, BI and ML
- Cost, performance and quality monitoring
- Deliberate knowledge transfer to your own staff
Who this is for
Leadership flying blind
Board decks assembled from spreadsheets that disagree with each other. You get one governed source of truth and numbers you can defend.
Companies with data silos
After growth or M&A, every system tells a different story. We unify the sources into one warehouse without ripping out the tools that work.
Product teams needing event analytics
You ship features but cannot see activation, retention or funnels. We build event pipelines and product dashboards on your own warehouse.
Organizations preparing for AI
Models are only as good as the data underneath. We build the governed foundation that makes AI projects cheaper, faster and auditable.
Tools we work with
dbt
Snowflake
BigQuery
Databricks
PostgreSQL
Airflow
Fivetran
Airbyte
Kafka
Spark
Looker
Power BI
Metabase
Great Expectations
Terraform
Go deeper
Five specializations under one data practice. Start where it hurts most.
Questions, answered
Do we need this before AI?
Mostly yes. AI is only as good as the data feeding it, and a clean, governed data layer makes models cheaper, faster and more accurate. We build the foundation AI actually needs.
How long until we see value?
First useful dashboards in weeks, not quarters. We ship a working slice early, then extend it. You get answers to real questions fast while the wider warehouse grows underneath.
Our data lives in a dozen systems. Is that a problem?
It is the normal starting point. Connectors like Fivetran and Airbyte cover most SaaS and database sources, and we build custom ingestion for the rest. Fragmentation is an argument for a warehouse, not against one.
Should we buy a tool or build a platform?
Usually both. We assemble proven components, warehouse, ELT and BI, and write custom code only where your business logic lives. You get the speed of buying with the fit of building, and you own the result.
Will it work with our existing stack?
Yes. We integrate with your warehouse, BI and cloud, whether that is Snowflake, BigQuery, dbt or Power BI. We meet your stack where it is, no rip and replace.
How do you keep data secure and compliant?
Access controls, encryption, lineage and audit built in from day one. We design for GDPR and your internal policy. Governance is part of the pipeline, not a bolt-on afterthought.
Make your numbers trustworthy
Talk to a senior engineer about your data landscape. You will leave the call knowing what to build first.
Book a discovery call