An AI data-preparation system
I'm building an AI-powered system to automate the repetitive parts of data preparation: turning raw, messy, inconsistent or unstructured data into clean, organised datasets for a specific training objective.
The machine does the bulk. A person signs off.
Automated cleaning makes mistakes. It can remove things it shouldn't, keep near-duplicates or break formatting. That's why human review is built into the workflow rather than added at the end, and why nothing counts as final until it has been checked against your requirements.
- Receive authorised dataOnly data you have the rights to share.
- AI cleaning and organisationCleaning, structuring and preparation by the system.
- Structured dataset for reviewOutput staged for inspection, not shipped.
- Human quality reviewMistakes, missing information, incorrect removals, remaining inconsistencies.
- Correct and validateFix issues; confirm the dataset meets requirements.
- Deliver or trainHand over the approved data, or use it in an agreed training workflow.
What the automation is for
Faster turnaround
Repetitive cleaning and structuring done by the system shortens the time from raw data to a reviewable dataset.
More capacity
As more of the work is automated, larger volumes can be processed without labour growing at the same rate.
Consistent quality
The quality bar is set by the human review step, not by the automation, so speed doesn't come at the cost of correctness.
Where it stands today. The data-preparation system is in active development and is used to assist my own work. It is not offered as a standalone, production-ready commercial platform, and its capabilities and automation level will be validated before they're advertised as such. Client datasets always go through human review regardless.