Ingestion and the data foundation
Before AI can read your data, something has to collect, clean, and shape it. That is the foundation.
Ingestion is the work of getting data from where it lives into a place you can use, and it’s most of the work. Sources are messy: databases, file shares, SaaS exports, event streams, PDFs in a dozen layouts. A pipeline pulls from each, on a schedule or in real time, lands it raw, and then models it into clean tables with types, keys, and contracts the next system can rely on. Every transformation is version-controlled, every run repeatable.
Everything downstream stands on this. Embeddings inherit the corpus, agents inherit the tables, dashboards inherit the definitions. When the foundation is missing we build it first, because tuning retrieval on top of a broken corpus is polishing the wrong layer.