How SMC can turn images into a faster first draft for sales.
The proof of concept uses an AI agent to read an incoming image, extract the relevant product details, and produce a well-formed API request to the PriceDuct application — ready to submit without manual data entry.
From incoming image to a ready-to-submit API request
A simple five-step view of how the proof of concept turns an image into a well-formed PriceDuct API request.
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1Receive
Take in an incoming product image.
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2Read
The AI agent analyzes the image and extracts product details.
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3Organize
Structure the extracted details into the fields PriceDuct expects.
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4Suggest
Validate the extracted data and flag anything unclear or incomplete.
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5Review
Output a well-formed PriceDuct API request, ready to submit or review.
How this differs from training a model on GPUs
This proof of concept is about using an existing AI capability inside SMC's workflow, not building a brand-new model from scratch.
Uses existing AI to bridge images and PriceDuct
For each image, the agent extracts product details, maps them to the right API fields, and flags anything that needs human confirmation.
Not a GPU training program
We are not collecting massive datasets and spending months training a new model. The value comes from better document handling, product matching, and review workflow around an existing AI tool.
Follow one image through the proof of concept
Click a step to see how one product image moves from intake to a ready-to-submit API request.
An image is submitted for processing. At this point, the product details have not yet been extracted.
Receive the image
The proof of concept starts with the current intake process, so no customer behavior needs to change.
That makes the trial easier to adopt and easier to evaluate.