Home / Case Studies / US Wholesale & Bulk-Supplies Retailer
/ Case Study · eCommerce
AI-Powered Product Enrichment & Catalog Automation for eCommerce. Scaling 20,000 SKUs in Minutes.
/ The engagement
Multi-Model AI Product Enrichment Engine
A multi-model AI pipeline coordinating Perplexity, OpenAI, and Gemini to turn raw SKUs into SEO-ready listings for Adobe Commerce, cutting enrichment time from 15–20 minutes to under 5 per SKU across 20,000+ products.
/ About the client
The client is a US B2C eCommerce retailer selling wholesale food products, janitorial supplies, party essentials, and more, sourced from third-party suppliers. With incoming inventory providing only basic codes and brief descriptions, expanding a 20,000+ SKU catalog created a major operational bottleneck.
Brainvire absorbed this catalog complexity by developing an automated multi-model AI enrichment pipeline. We digitized product research, SEO content, pricing intelligence, photo selection, and content validation into one backend solution feeding straight into Adobe Commerce.
/ The approach
A parallel, multi-model AI pipeline delivery approach.
Data Structure Analysis
Mapped raw supplier data formats and defined target Adobe Commerce listing schemas.
Multi-Model AI Architecture
Assigned specialized tasks to Perplexity, OpenAI, Gemini, and custom ML models.
Pipeline & Validation Build
Engineered parallel automated tracks for research, writing, image selection, and claim checks.
Batch Execution & Storefront Feed
Executed multi-SKU batch processing and automated structured data exports.
/ What stood in the way
Four hurdles between raw supplier spreadsheets and an enriched storefront.
Select a challenge to see how it shaped the build.
High-Volume Catalog Enrichment
Manually researching and writing listings took 15 to 20 minutes per SKU, creating publishing delays across a growing 20,000+ SKU catalog. Brainvire orchestrated a two-stage AI research and generation pipeline using Perplexity for web context and OpenAI for structured, SEO/GEO-optimized titles, descriptions, FAQs, and breadcrumbs, reducing enrichment time to under 5 minutes per SKU.
Time-Consuming Image Curation
Reviewing 12+ supplier photos per product to manually select front, back, and packaging views was extremely resource-intensive. Brainvire integrated an AI computer-vision classification model that automatically reviews raw supplier assets, discards low-quality shots, and selects the top 3 representative images per SKU.
Risk of Unvalidated AI Content
Generating content via AI web research risked publishing unsupported claims, unit errors, or copyright issues to the live storefront. Brainvire implemented a dedicated validation layer powered by Google Gemini, systematically scanning generated content for factual inaccuracies, formatting glitches, and compliance risks before final delivery.
Opaque Competitive Pricing
Setting competitive market prices manually across thousands of items delayed product onboarding. Brainvire introduced an automated machine-learning pricing model that analyzes live market benchmarks and competitor context to suggest optimal retail prices directly within the enrichment pipeline.
/ Product screens
The automated catalog pipeline.
The backend data transformation flow powering the wholesaler's catalog.
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/05/ The results
What changed, in numbers.
/ Next step
Is catalog complexity posing a challenge to your storefront expansion?
Brainvire helps enterprises leverage multi-model AI pipelines to automate product enrichment, optimize SEO, and scale storefronts effortlessly. A thirty-minute working session, no sales deck.
Book a Working Session →