Indian D2C lifestyle brand Chumbak (GOAT Brand Labs portfolio) refreshes listing visuals to a strict 8-artboard template. An AI pipeline with self-service replaced a chain of six contractors per SKU.
Chumbak manages a catalog of ~3,000 active SKUs across 100+ categories — from totes and wallets to ceramics and copper bottles. Channels: chumbak.com + global.chumbak.com, Flipkart / Myntra / Amazon.in / Snapdeal, and 50+ retail stores. Every collection requires a scalable package: packshot, lifestyle, infographics, motif details.
Following the GOAT Brand Labs portfolio acquisition, the brand operates under operational pressure — levers are sought through unit economics and speed. The listing production cycle turned out to be one of the costliest and slowest nodes.
A full package for a single SKU (5–6 images in an 8-artboard composition) took 5–7 days and cost $30–80 per image. For a catalog of 3,000 SKUs with 30–40% annual refresh — 15,000–18,000 images and a budget of $0.4–1.4 million per year for production alone.
Production was distributed across multiple agencies and orchestrated by a single content manager; up to six contact persons were involved per SKU. Off-the-shelf generative models "drifted" on dense ornamental prints and distorted the geometry of ceramics and wood, destroying brand recognizability.
An AI pipeline refreshes D2C listing visuals: packshot → lifestyle → infographic, following a strict template with brand compliance checks and self-service access for agencies.
An image-to-image pipeline on a fine-tuned diffusion model trained on Chumbak's brand visual corpus was deployed for the priority pool. The content manager uploads a reference packshot → the system generates 5–6 angles across four templates. $1,000/month for the pilot vs. $8,000–16,000 in photo production — a significant cost reduction, cycle from 7 days to 24 hours.
The color-preservation pipeline verifies each image against the reference palette for the SKU from the PIM (ΔE ≤ 5 in CIELAB), checks for brand motifs, and verifies texture correctness. If the image fails, it is returned to the queue with a refined prompt — no manual rework; the brand auditor sees a deviation heatmap.
An interface with a role model (brand manager, brand auditor, agencies — each with their own access scope), API rate limits, and an audit log. Multiple agencies work in parallel: significant throughput increase, the single-point-of-failure on one manager is eliminated, SLA for returning a package goes from 3 days to 4 hours.
The model is fine-tuned on an expanded corpus — all 100+ categories with emphasis on new texture classes (painted ceramics, wooden tableware, copper bottles, embroidered textiles). The self-service infrastructure scales without linear headcount growth on the vendor side.
The self-service mode, proven with Chumbak, is ready to be rolled out across 20+ brands in the GOAT Brand Labs portfolio — each connects via its own template configuration on top of the shared 24.online core.
This changes the ROI calculation for GOAT: production economics scale without linear vendor headcount growth, and Chumbak brand management is freed up for strategic work on the international catalog.
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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