Service
AI-ready data & MLOps
Most AI projects do not fail on the model. They fail months earlier, on data that was never ready for it. AI-ready data means your tables are clean, documented, traceable and structured so a model or an analyst can use them on day one. This service builds that layer, plus the MLOps plumbing around it, so machine learning starts with training instead of wrangling.
What gets built
The starting point is a governed Gold layer, the business-ready data described on the lakehouse page. From it come the assets AI actually consumes: feature stores for classic machine learning, embeddings for retrieval and RAG, and analytics layers that answer the questions your team asks most. All of it built on BigQuery or Microsoft Fabric, with the same engineering discipline as the rest of the platform.
Lineage runs through everything. Every feature, every training set and every embedding traces back to the source it came from, so when a model gives a strange answer you follow the trail instead of guessing. That traceability is what makes data AI-ready rather than merely available, and it is what auditors and governance frameworks increasingly ask to see.
The same work pays off for BI immediately. The analytics layers that feed models also feed dashboards, so you do not run one stack for reporting and another for AI. One governed set of numbers serves both, and every improvement in data quality shows up in both on the same day.
How it works in your team
I work embedded in your team, end to end. If you have data scientists, they get a platform where experiments are reproducible and shipping a model is a pipeline, not a ceremony. If you do not, we scope which use cases justify machine learning at all and make the data AI-ready so the option is real when you want it. Either way the work lands in your repositories with docs and runbooks, and the handover leaves your team in control.