AI answers from manual photos and tables

Syntalith can scope Company Brain work around selected visual manuals and presenting answers alongside the relevant page, image, or table. Your team identifies questions that currently require manual browsing. It can review an answer together with the passage or visual that supports it. This helps determine whether the work calls for preparing the material, adjusting an existing application, or connecting additional sources.

When the caption changes what a photo means

Suppose an employee is preparing display materials for a store. A guide shows photos of two similar stands, with a table listing inserts for each version. The question is, “Which insert is shown with the wide-base stand?” Answering requires connecting the photo to its caption and the corresponding table row.

A useful application gives the name recorded in the document and shows the employee the photo and table behind it. If the caption is unreadable or the document does not establish the variant, the answer should explain that and point to the person responsible for the material. The employee should not receive a confident name selected merely because a nearby paragraph sounds relevant.

A link to the entire manual leaves much of the work to the user. For a long file, opening the page containing the illustration is more helpful. Whether the application can point to that exact location depends on the document format and the current tool, so it is worth seeing this before expanding the scope.

A table needs its headings

Text copied from a table can leave names and values next to each other without preserving their relationship. A heading spanning several columns or a note below the table may determine what a row means. Similarly, an uncaptioned photo may show several components when the question concerns just one.

Preparing the library may involve improving image descriptions or supplying a more legible file. Someone familiar with the product helps assess whether the displayed material supports the answer. If the documents are inconsistent, the application should not guess at a missing relationship. The document needs correction, or the scope of supported questions needs to be narrower.

Microsoft describes the Layout model in Azure AI Document Intelligence as extracting text, tables, figures, and document structure. This capability can form part of source preparation. Recognizing elements on a page does not by itself establish that an assistant has interpreted a manual correctly. Microsoft documentation.

When a better manual is enough

If employees use a few short guides, a useful table of contents, clear captions, and a readable viewer may solve the problem. Those improvements are worthwhile regardless of any AI plans. Asking an assistant questions will not repair a blurry scan or explain a label the author never defined.

An additional application becomes useful when similar questions span many documents and employees do not know where to look. We can discuss retrieval and passage display for a particular document family. Your company reviews answers with both someone who knows the material and an employee who will use it. The latter may notice that even the correct passage is difficult to read at their workstation.

Bring a question that text alone cannot answer

Tell us about a question that requires looking at a photo or table and the tool where your manuals are stored. That is enough to start discussing a possible engagement. See our pricing page for engagement terms.