GenAI Bill-of-Materials Automation Pipeline

Designed and deployed a GenAI pipeline at Tiger Analytics to automate Bill-of-Materials (BoM) generation for lighting parts — replacing a largely manual, time-intensive process.

Problem: Generating structured BoMs for lighting components required engineers to manually extract and organize part specifications from unstructured documents, taking significant time per product.

Solution:

• LLM-based structured extraction pipeline using AWS Bedrock to parse product datasheets and specifications
• Experiment tracking and model versioning with MLflow
• Containerised with Docker for reproducible, production-ready deployment
• Output structured BoMs with part names, specifications, quantities, and supplier info

Key Results:

• 80% reduction in manual BoM generation time
• Consistent structured output across diverse document formats (PDFs, HTML specs, Excel sheets)

Tech Stack: Python AWS Bedrock MLflow Docker Prompt Engineering Structured Extraction

Impact: Freed up engineering time from repetitive data entry, enabling faster product configuration and procurement workflows.