A leading e-grocery player with its own dark stores is pivoting from the premium segment to the mass market. Manual photo processing in Photoshop to meet a unified standard became the bottleneck for assortment onboarding.
The client is part of a large fintech ecosystem, with proprietary dark stores and logistics. A strategic pivot from premium to the mass market is accelerating category launches and catalog rotation.
The in-house photo standard requires a consistent look (grey background #F5F5F5, 10% inset from edge, studio shadow, front-facing angle, different rules per category). Volume and source diversity turned compliance into a bottleneck.
The flow is 400–600 items per week (often 2 photos per item), with a peak of 820 items over 3 days before a holiday. The content team cannot keep up with new arrivals, while a backlog of thousands of outdated listings not meeting the standard accumulates in parallel.
Manual processing in Photoshop takes ~3 minutes per listing (remove background, fill #F5F5F5, align with 10% inset, add shadow, resize). Macros failed on insets. Photos arrive from 6 different sources, some being phone shots from branches without a photographer.
An AI platform mass-processes product photos to a unified standard: background removal, studio shadow, cropping, and batch processing of thousands of SKUs.
Each image passes through a chain of four modules: background removal (U2Net) → studio shadow → quality enhancement (CLAHE) → cropping. Specialized algorithms rather than a general-purpose generative model — no hallucinations or text distortion on packaging. ~15 seconds per listing instead of ~3 minutes.
The system determines orientation from the object mask and applies the appropriate template: an elliptical shadow at the base for upright products (bottle, milk carton) and a distributed shadow for horizontal products (bread, a flat package). The standard is met on every image without designer involvement.
Content arrives from 6 sources (Google Drive links, ZIP archives, drag-and-drop, brand databases, phone shots from branches, supplier materials) and is unified into a single stream with naming by barcode or article number. The team no longer works in crisis mode before peak periods.
A background scan evaluates each photo on 5 parameters (background, insets, angle, shadow, sharpness) and assigns a standard compliance score, forming a prioritized queue: lightweight pipeline, full cycle, or manual review for critical deviations.
The platform became the automation backbone for the entire content block, not just a photo editor. Tracked next steps include: product videos for private labels, ERP integration by listing status, and automated content intake from suppliers.
The content team is no longer the assortment onboarding bottleneck — this unblocked the client's strategic pivot to the mass market, where new arrival speed competes directly with marketplaces.
The main pain was not uploading photos, but processing them: we tried running Photoshop at scale, but the guide alignment and insets kept going off every time. Now the standard is applied to every listing automatically, and the team doesn't work in crisis mode before peak periods.
The metrics and results presented reflect outcomes of a specific project and depend on its initial conditions. They are provided for informational purposes only, do not constitute a public offer, and do not guarantee similar results in other projects. Supporting materials are available on request.
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