Engineering across the full data lifecycle
Six disciplines that connect into one delivery team — from raw source systems through to the AI and dashboards your business runs on.
Data Engineering
We design and build the pipelines that move data from your source systems into a form your teams can trust — batch, streaming, or both.
Full detailsCommon challenges
- Data spread across multiple databases, flat files and APIs with no single source of truth
- Manual effort to prepare recurring reports
- Pipelines that break silently or require constant babysitting
What we deliver
- Production pipelines with monitoring and alerting
- Documented data flow and lineage
- Runbooks for operating and extending the pipeline set
Cloud Data Platforms
We design cloud data warehouses and lakehouses that hold up under real query load, with governance and cost control built in from the start.
Full detailsCommon challenges
- Legacy warehouses that are slow, expensive, or hard to extend
- No consistent modeling standard across teams
- Cloud spend growing faster than the value it delivers
What we deliver
- Warehouse or lakehouse environment sized to workload
- Modeled, documented schemas
- Migration plan and cutover for legacy replacement
Business Intelligence
We build BI on top of governed semantic models so numbers match across every dashboard — not pixel-perfect charts sitting on unreliable data.
Full detailsCommon challenges
- Dashboards that are slow, inconsistent, or trusted by no one
- DAX and models built without a plan for scale
- No row-level security or workspace governance
What we deliver
- Governed semantic model as the single source of truth
- Executive and operational dashboards
- Documented refresh, security and access model
AI and Machine Learning
We build machine learning and AI on the data foundation we've already made reliable — from forecasting models to an AI insight layer over existing dashboards.
Full detailsCommon challenges
- Trends and anomalies buried in dashboards nobody has time to read closely
- Analysts spending hours writing commentary by hand
- No consistent way to share insight across teams
What we deliver
- Trained, monitored models in production
- Documented model assumptions and limitations
- Insight layer or scoring output wired into existing dashboards
DevOps and Platform Engineering
We bring a Git-centric CI/CD workflow to data and analytics — so pipeline, model and dashboard changes deploy the same reliable way application code does.
Full detailsCommon challenges
- Manual deployment steps prone to human error
- Infrastructure changes with no version control or reproducibility
- Limited visibility when a data job fails
What we deliver
- Automated CI/CD pipeline for data and analytics assets
- Infrastructure as Code for reproducible environments
- Monitoring and alerting dashboard
Data Strategy and Consulting
We audit what you have, recommend an architecture that fits your scale and budget, and — where useful — prove it out with a focused proof of concept before a full build.
Full detailsCommon challenges
- Uncertainty about which platform or tools fit the organization's scale
- Cloud and tooling costs that have grown without a clear driver
- No documented architecture or roadmap to align stakeholders
What we deliver
- Architecture assessment document
- Prioritized roadmap
- Cost optimization recommendations with estimated impact
Let's talk about what your data should be doing for you
Tell us where you are today and where you're trying to get to. We'll respond with a clear, honest read on the path forward.