Service 04
Data & AI Infrastructure
Pipelines, stores and serving paths a model can actually depend on.
- Pipelines
- Storage
- Serving
A model is only ever as good as the worst thing feeding it.
Most problems that arrive labelled as AI problems are data problems in a costume: the field is missing on a third of the rows, two systems disagree about the same customer, the nightly export failed on Sunday and nobody was told until Thursday. No amount of prompting repairs that.
We build the layer underneath — ingestion, validation, storage, indexes, the job that runs at four in the morning and the alert that fires when it doesn’t.
What we actually do
- Ingestion from the systems you already run, with retries and no silent loss
- Validation at the boundary, so bad data is caught where it enters
- Storage chosen for the query pattern — relational, vector, or both
- Embedding and index pipelines that can be rebuilt from scratch on demand
- Serving paths with a latency figure you can hold a promise to
- Alerts when a job fails, on the day it fails
Who it’s for
Teams whose numbers disagree depending on who is asked, and teams whose model is being fed by an export somebody maintains by hand.
How it runs
Two to eight weeks depending on how many systems are involved, staged so each stage is useful on its own.
Start with the problem.
Tell us what you’re working on and whether data & ai infrastructure is really what it needs. An engineer reads every enquiry.