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AI-ready data and document retrieval
AI retrieval is only as good as the documents behind it. An assistant that searches your own material gives weak or unsupported answers when files are duplicated, out of date, badly named or missing basic metadata.
Getting ready means deciding which questions the system must answer, cleaning and structuring the source set, adding the metadata retrieval depends on, and testing answers against known sources before anyone relies on them. These guides cover each step. We build data systems where every claim traces back to its source, and we can run them for you.
Guides
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Start here
How to get your documents ready for AI retrieval
A step-by-step method for making a document library readable, structured and traceable before any AI tool searches it.
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Which metadata fields matter for AI retrieval
The metadata fields that let an AI knowledge base filter the right sources and trace every answer to a page or row.
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How to test an AI knowledge base before your team uses it
A test plan for AI knowledge bases that checks sources, retrieval, citations and refusals before people rely on the answers.
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Why AI gives weak answers when the source material is messy
How to tell whether a weak AI answer comes from the model or from old, duplicated, vague or conflicting source documents.
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South Africa's withdrawn AI policy: a lesson in citations
The dated record of South Africa's withdrawn draft AI policy, and the citation checks that stop invented references reaching a public draft.