Landing AI in Structured Document Extraction

Landing AI passed this scenario by returning an honest null for the absent field. The metadata showed no source range for that field. The receipt number was returned as RHS-70234, with a source range shown in the metadata.

Landing AI passed this scenario. It returned the correct owed amount from three documents with different layouts: $90.10 from the receipt, $227.33 from the invoice, and USD 157.50 from the other receipt. The screenshots show each value highlighted in the viewer and the same single String field in the schema.

Landing AI passed this plain-field extraction case. It returned the receipt number as a string, and the source span was highlighted on the page; the input showed one receipt number and no competing same-type value nearby.

Landing AI derived the requested value in this scenario. The invoice shows DISTANCE 450 miles and FUEL RATE £0.18 per mile; the JSON returns fuel_cost as GBP 81.00, and the document text does not contain 81.00. The playground screen shows the same result beside a one-field schema.

Landing AI extracted the repeated set of records in this run. The invoice table on page 3 returned three line items with the correct values and pairing; the delivery note says there were three lines and six units, and the schema asked for description and amount only.

Landing AI left the non-record out of the extracted set in the tested invoice. The output contained five members, matching the five numbered rows, and it did not include the separate Total row or its 1,982.00 amount. The schema description shown in the capture was neutral.

Landing AI kept the repeated set in one array when the data crossed a page break. In the checked case, ten line items appeared in document order with no duplicate at the boundary, and the results panel showed the array opening once and closing once. No other case is reported here.

Landing AI returned an empty set when the document said there were none this period. In the checked PDF page, the source text said no damage or exceptions were reported, and the export showed an empty array. The schema pane showed the field as a required array, and the output did not turn the marker text into a member.

Landing AI failed this mixed digital-and-scanned PDF case. All four fields came back, and the scanned-page values were exact, but the total due returned as "#1,246.80" even though the page text and rendering show "£1,246.80"; the run also carried both parse and extract ids.

Landing AI fails to total across uploaded documents. Chat answered with one file’s own amount or refused, and Extract returned each invoice’s own total in the cross-document field instead of one combined sum.

Landing AI failed to filter records across extracted documents. Chat refused the request, and the interface only showed one document at a time with no filter or query control; the receipts themselves were extracted correctly, including two above 150 and one below.

Landing AI passed this scenario. It answered the technician question with Renata Kowalski and showed clickable references to the receipt’s page text, while the receipt itself prints that name. Chat is experimental, works on one file at a time, and has no API, so the result is scoped to the web app.

Landing AI passed this scenario by returning an honest null for the absent field. The metadata showed no source range for that field. The receipt number was returned as RHS-70234, with a source range shown in the metadata.