For search, every document is cut into small pieces of text. Before that, we clean it up. Three examples from a client project with regulated technical documents:
Page headers and page numbers end up as tiny pieces of their own in the index.
Search finds empty snippets instead of the passage with the answer.
Step: remove page headers and page numbers
The product name at the top of every page is taken for a heading.
Pieces of text sit under the wrong heading. The AI puts a fact in the wrong section.
Step: stop treating the product name as a heading
Section headings get lost when the document is read in.
The AI cannot tell which section a fact comes from, so it cannot cite it.
Step: rebuild headings from the official numbering
Each step is small. Together they decide whether the AI finds the right passage. Windtunnel measures each step on its own, so you see which one is worth it.
Small samples: one document for the first two examples, ten documents of the same type for the third. They show what each check catches, not an average. Client and documents not named.
No slides. No demo. Five questions on your system, and an honest answer on fit.
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