Staff spent 13 minutes standardizing each product. AI now generates brand-aligned HTML in 2 minutes, freeing teams for higher-value work.
A supply management and distribution company maintains a catalog of 30,000 essential items, adding 300 new products monthly from manufacturers with varying data formats.
Staff manually standardized inconsistent product data to meet brand voice and SEO requirements, a slow process that throttled catalog updates. Early...
“Each manufacturer sends us their product data in different formats and we have several collaborators who read and standardize this data, creating product descriptions in our catalog, especially the online one.”
B2B and retail distributor of office, cleaning, and indirect supplies.
Built Gimba's initial SageMaker prototype and managed its transition to Amazon Bedrock
Cloud computing platform and on-demand infrastructure services.
Gimba's Product catalog management is part of this use case:
Related implementations across industries and use cases
Manual tagging bottlenecked inventory for a month. AI now analyzes images to generate accurate descriptions in just one hour.
Scattered systems left a long tail of manual actions untouched. AI agents now operate UIs directly to run workflows, escalating exceptions.
Shoots took 10 crew members half a day. One employee now generates complex scenes—like shoes on wet floors—in 10 minutes with AI.
Sellers spent 10 minutes researching and drafting listings. AI now generates them from a photo, cutting the work to seconds.
Manual reviews for artisan products took days. Gemini now generates listings from a single image, cutting approval to <1 hr.
300+ receipt layouts forced manual entry. GenAI now reads raw images with 98.6% accuracy, cutting costs 90%.
Employees previously broke work into rigid tasks. Now, they delegate high-level goals to agents that work autonomously for hours.
Data scattered across systems forced generic content. Now, AI agents surface personalized stats and videos via chat during live matches.
Translating updates one language at a time bottlenecked distribution. Now, AI drafts multilingual commentary in minutes for editor review.
Staff spent 13 minutes standardizing each product. AI now generates brand-aligned HTML in 2 minutes, freeing teams for higher-value work.
A supply management and distribution company maintains a catalog of 30,000 essential items, adding 300 new products monthly from manufacturers with varying data formats.
Staff manually standardized inconsistent product data to meet brand voice and SEO requirements, a slow process that throttled catalog updates. Early...
“Each manufacturer sends us their product data in different formats and we have several collaborators who read and standardize this data, creating product descriptions in our catalog, especially the online one.”
B2B and retail distributor of office, cleaning, and indirect supplies.
Built Gimba's initial SageMaker prototype and managed its transition to Amazon Bedrock
Cloud computing platform and on-demand infrastructure services.
Gimba's Product catalog management is part of this use case:
Related implementations across industries and use cases
Manual tagging bottlenecked inventory for a month. AI now analyzes images to generate accurate descriptions in just one hour.
Scattered systems left a long tail of manual actions untouched. AI agents now operate UIs directly to run workflows, escalating exceptions.
Shoots took 10 crew members half a day. One employee now generates complex scenes—like shoes on wet floors—in 10 minutes with AI.
Sellers spent 10 minutes researching and drafting listings. AI now generates them from a photo, cutting the work to seconds.
Manual reviews for artisan products took days. Gemini now generates listings from a single image, cutting approval to <1 hr.
300+ receipt layouts forced manual entry. GenAI now reads raw images with 98.6% accuracy, cutting costs 90%.
Employees previously broke work into rigid tasks. Now, they delegate high-level goals to agents that work autonomously for hours.
Data scattered across systems forced generic content. Now, AI agents surface personalized stats and videos via chat during live matches.
Translating updates one language at a time bottlenecked distribution. Now, AI drafts multilingual commentary in minutes for editor review.