Data Foundations & AI-Ready Databases
Databases, lakehouses, and vector stores engineered for AI workloads.
What you get
The number one reason AI projects fail is unprepared data. We modernise data architecture, build feature stores and vector indexes, and stand up the analytics platforms your models depend on.
- Lakehouse on Databricks, Snowflake, or BigQuery
- Vector store engineering (pgvector, Qdrant, Pinecone)
- Feature store with lineage and freshness SLAs
- Data quality and observability tooling
Typical timeline
8–16 weeks
Engagement model
- Fixed-scope or T&M, your call
- Weekly demos against named KPIs
- Production checklist before handover
- Optional managed services from week one
Ready to start
Turn one AI use case into measurable production value.
Book a 30-minute consultation. We will walk through the use case, sketch the value case, and tell you honestly whether we can help.