Craft · Data quality
Data quality
High-quality, structured data is the fuel everything else runs on. Before a model can work reliably, the sources have to be right, clean, current, and clearly owned.
Real sources, not a clean export
Your most valuable data rarely sits in a tidy database. It lives in quotes and contracts, on SharePoint and network drives, in Teams and email threads, and in the responses your line-of-business systems return through their APIs. We take these real sources as they are, instead of waiting for an ideal dataset that will never exist.
Cleaning and structuring
Raw data is contradictory, duplicated, and out of date. We remove duplicates, align terminology, and bring documents into a structure a model can read reliably, with source attribution, so every statement stays traceable back to where it came from. Scattered company knowledge becomes reusable, verifiable building blocks.
Ownership and currency
Data decays. Without clear ownership, a good knowledge base turns into a misleading one within months. For each source we define who maintains it and on what cadence it is refreshed, so the model works from today's state rather than last year's. Bad data in means unpredictable AI out, maintained data is the lever against that.
Do you know what your AI is reading?
In the AI Readiness Audit we map your data sources, their condition, and their risks, and show where data quality gives you the biggest lever. Book the audit as your first step.