Business work rarely arrives as one clean paragraph. A receiving issue may include a photograph, a delivery document and a short note from an operator. Multimodal input design organizes those pieces so an AI application can interpret the request without losing their source, sequence or business meaning.
Specify the decision before collecting inputs
For an illustrative goods-receipt review, the task might be to identify the delivery reference, summarize the reported damage and flag fields that require checking. It should be clear whether the output is a review draft or an authorized inventory transaction.
Define the required evidence for each field. A photograph may show visible damage; the purchase order establishes the ordered item; a receiving record establishes an accepted quantity. Do not ask one modality to establish facts that belong to another source.
Package context with each file
Attach a record identifier, capture date, source and purpose to each input. Label photographs by view and documents by type. Keep the original available for review, and record any cropping, rotation or transcription performed before processing.
OpenAI’s vision documentation describes image analysis and limitations including difficulty with some small text, spatial judgments and incorrect descriptions. Use clear images and test the actual documents and camera conditions expected in the workflow. Include an explicit option to return an unreadable or uncertain field.
"In the past, we managed software; today, we design autonomous processes that AI executes for us. The future belongs to those who automate not just the tasks, but the decision-making itself."
Preserve document structure
Tables, page order and diagrams can affect interpretation. OpenAI’s file-input documentation explains that PDF handling on vision-capable models can include both extracted text and page images. Supported input behavior depends on the file type and selected model.
Test whether the application associates a quantity with the correct row and page. Avoid combining unrelated documents without clear labels. For every extracted value, retain a reference to the source file and relevant page or image so a reviewer can resolve disagreements.
Validate before changing business records
Check extracted identifiers against master data. Reconcile quantities and dates with the relevant ERP transaction, then route missing or conflicting evidence to the responsible person. Treat a generated confidence score as another model output to evaluate, rather than assuming it measures the true likelihood of correctness.
Build a test set containing blurred images, missing pages, repeated item names and conflicting notes. Measure field accuracy and time spent reviewing exceptions. GreatWork’s module catalog lists Documents and MediaLibrary; confirm the intended storage and access arrangements during discovery. Its data services and artificial intelligence services provide related areas for planning the complete input-to-review process.
I look forward to seeing how these developments will improve service levels and customer satisfaction in the freight industry!