05 / Data Engineering
Data platforms & pipelines
Pulling your data together, cleaning it up, and storing it somewhere useful, sized to what you actually need, not an oversized system built for a scale you don't have.
Who this is for
Teams where the AI model isn't the blocker, the data is. Systems that don't talk to each other, a "database" that's really a folder of spreadsheets, or a pipeline that breaks every time something upstream changes.
What usually goes wrong
Most AI projects stall on messy or scattered data, not on the model. We size the system to your actual problem: a well-built pipeline into a right-sized database, not an oversized architecture built for a scale you don't have.
What you get
- Pipelines that pull data in from your actual sources, apps, databases, files, live event feeds
- A cleanup step with automatic checks, so a change upstream fails loudly instead of silently corrupting your data
- A database or storage system sized to your current and near-term needs
- Documentation a future engineer, ours or yours, can actually follow
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