AI in data workflows

Much of the cost of running a data product sits in the workflow: getting data in, cleaning it, checking it and getting it out. We apply AI where it removes real effort, and keep people in control where quality matters.

• Ingestion and extraction - pulling structured data out of documents, feeds and legacy sources.
• Cleaning and normalisation - fixing, standardising and de-duplicating at a scale people can't.
• Metadata and tagging - consistent, auditable classification across large collections.
• Quality assurance - automated checks, sampling and review queues so errors are found early.
• Human-in-the-loop design - clear points where people review, correct and approve, so the workflow gets better over time.

• Teams running data operations with heavy manual effort
• Publishers and data owners with large back-catalogues to structure
• Organisations that need AI-assisted workflows that are auditable and measurable
• Leaders who want cost and quality gains without a big-bang rebuild

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