Quotes & contracts
PDFs and Word documents holding your most valuable knowledge, unstructured.
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.
§01 Craft
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.
SharePoint & drives
Grown file shares full of duplicates, versions, and outdated states.
Teams & email
Decisions and reasoning, scattered across threads and inboxes.
Systems & APIs
ERP, CRM and industry tools whose responses can be collected in structure.
§02 Craft
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Raw data is contradictory, duplicated, and out of date.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.
- 01 Take raw data as it is
- 02 Remove duplicates & contradictions
- 03 Align terminology
- 04 Structure & source attribution
- 05 Verifiable knowledge blocks
§03 Craft
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.
Bad data in means unpredictable AI out. Maintained data is the lever against that.
§04 Read on
More on data quality
How bad data turns into unpredictable AI, and how you counter it, we go deeper in the blog and in the open mema research.