ecommerce_ops · saas · workflow
GoDaddy builds a scalable AI product categorization system using Amazon Bedrock batch inference
GoDaddy's existing product categorization used an out-of-the-box Meta Llama 2 model across six million SKUs, but the generated categories were often incomplete or mislabeled, and running individual LLM calls per product was too costly at scale.
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 · Product JSONL uploaded to S3
A JSONL file containing product data is uploaded to an S3 bucket, triggering the first Lambda function.
Tools used
Amazon BedrockLangChainPydanticOutputParserOutputFixingParser
Outcome
The batch inference solution achieves 97% category coverage on both the 5,000 and 100,000 hold-out test sets, processes 5,000 products in 12 minutes (80% faster than the 1-hour requirement), and is 8% more affordable than the Llama2-13b proposal while providing 79% more coverage.
What failed first
The out-of-the-box Meta Llama 2 approach produced categories that were frequently incomplete or mislabeled, and the per-product API call approach was prohibitively expensive for large-scale deployment.
Results
Time saved12 minutes
Volumesix million products
Cost replaced8% more affordable
Grounding & classification
Source type: technical build writeup
33 fields verified against source quotes, 5 dropped as unverifiable.
content generationdata extractionproduct catalogfailure mode describedhuman review describedmetric backednamed customerworkflow describedsoftwareaccuracy improvementcost reductioncycle time reductionthroughput increasetechnical build writeupdata entry opsecommerce opsdocument to recordextract classify route