Data engineering
Data engineering for AI is still data engineering
Embeddings do not forgive a broken pipeline. Lineage, contracts, and serving layers are how Australian enterprises make models trustworthy.
The unsexy truth: most “AI failures” we are asked to look at are freshness, joins, and access control wearing a lab coat.
A model cannot be more trustworthy than the table it retrieved from. If that table is a nightly dump with no owner, your agent will be confidently wrong at 9:12am, every day, after the source system moved.
Treat data as a product
- A named owner.
- A contract: schema, grain, freshness SLO.
- Tests that fail the pipeline, not a dashboard someone ignores.
- Lineage you can explain to a regulator.
This is not new. What is new is that retrieval-augmented systems and agents will amplify whatever you already have.
What “data for AI” actually adds
Chunking, embeddings, eval datasets, and a serving path that is not “the analyst’s notebook.” Feature stores if you have a real scoring problem. A corpus with retention that legal signed.
Do not start with a vector database. Start with “which documents are we even allowed to embed, and who updates them.”
Australian specifics
Residency, APP, and sector rules show up as pipeline design, not as a policy PDF. If the embedding index cannot be rebuilt in-country from a documented source, you do not have a system. You have a souvenir.
We build this on the clouds you already pay for. Azure and AWS will both work. Staffing the on-call will decide which.
If you want agents, fund the pipes. It is the highest-leverage unsexy work left in the stack.
