From Print to Digital: Making Weekly Flyers Shoppable at Instacart Through Computer Vision and LLMs
Instacart's manual flyer digitization process required 3–4 hours per flyer and became unsustainable as dozens of retailers adopted weekly flyers, creating hundreds of hours of work each week and requiring retailers to submit flyers well ahead of their go-live date.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Retailer uploads flyer
A retailer uploads their weekly or monthly promotional flyer to the Instacart platform.
Tools used
SAMLLM
Outcome
The two-phase pipeline reduced flyer processing from 3–4 hours to under 30 minutes — a 10x reduction — while achieving 75–90% bounding box accuracy and 95% product recall in the top position, enabling Instacart to scale digitization across its entire retail network.
What failed first
Off-the-shelf ML solutions each had critical limitations: multimodal LLMs produced imprecise bounding boxes for complex flyers, traditional segmentation and contour detection models generated excessive noise, and existing solutions like FoodSAM fell short of handling the breadth and variety of retail flyer products.