Build
You know what you need and want it built properly. I design it, build it with your engineers, document it, and leave it running in your accounts. Your team owns it on day one.
What a build usually covers
- A metadata catalog. Ownership, lineage, and search across your warehouses, so people can find data and know whether to trust it.
- Cost controls. FinOps dashboards, workload policies, auto-suspend and auto-scale tuning, tagging, and budgets, so spend has an owner.
- A semantic layer. Governed metric definitions in one place, so dashboards and AI tools stop disagreeing about revenue.
- A path from model to production. Feature pipelines, a model registry, scheduled scoring, and CI gates, so models stop living in notebooks.
- Data quality you can enforce. Rules with SLAs, monitoring and alerting, and CI checks that stop bad data before it reaches a dashboard.
How it runs
Before any work starts, the scope fits on one page: the problem, what done looks like, the timeline, and the price. If anything changes, the page changes first.
I build alongside your engineers, in your repositories and your cloud accounts, with a short written update every week. Nothing runs on my infrastructure, and nothing is saved for a big reveal at the end. When the build finishes, it runs under your credentials, it is documented, and at least one of your engineers has worked on every part of it.
Why build with your team
A platform built by an outsider and handed over at the end is a platform nobody can change. Building alongside your engineers costs some time in the first sprint and pays it back every month after, because the people who will maintain the system watched it get built and know why each decision was made.
Related
- Case study
70,000+Cataloging 70,000 datasets across two data platforms
A publisher with several studios and two data platforms had no single place to find a dataset or see where it came from. A structured proof of concept picked the catalog, and the rollout finished early.
Read it - Case study
99.8%Keeping ML training pipelines at 99.8% uptime
A semiconductor manufacturer's SaaS MLOps venture needed pipelines that customers could count on. They ran at 99.8% uptime while volumes grew across fabs.
Read it - Article
Do we need a semantic layer before we add AI to analytics?
If you want AI to answer questions about your metrics, yes. What a semantic layer does, why AI makes it urgent, and how to start with the metrics in your board deck.
Read it
Not sure which fits?
Thirty minutes, free. We will work out whether this is the right shape of engagement, or whether another one is.