What it covers
AI is only as good as the data it can reach and the platform it runs on. Before we build, we make sure the foundations are there; where they aren't, we fix the pieces that matter for the roadmap rather than boiling the ocean.
- Data inventory: what exists, where it lives, who owns it, what's allowed to be used
- Quality and freshness assessment for the data your AI use cases depend on
- Pipelines and indexing for retrieval, including document processing and embeddings
- Platform selection: cloud AI services, model gateways, vector stores, orchestration
- Cost modeling so nobody is surprised by the inference bill
- Environment setup: dev, staging, and production for AI workloads with proper isolation
What you get
A clear picture of what's ready and what isn't, the pipelines and platform pieces the roadmap needs, and a cost model you can defend.
Common questions
Do we need a data warehouse or lakehouse first?
Usually not for the first use case. We'll tell you when the answer is yes.
Do you do traditional data engineering?
We do the data engineering that AI use cases need. For a broad data platform program we'll be honest about scope and bring in partners if it makes sense.