Enhancing Chemical Sales at ChemDirect with AI DataOps
Summary of the Problem
Managing and Standardizing the Data
ChemDirect, a third-party marketplace, faced significant challenges in managing and standardizing data for a vast number of chemical and non-chemical products sourced from multiple suppliers. Key issues included:
- Managing numerous unique properties that vary in importance across different industries
- Inconsistent product grouping from suppliers
- Searchability issues due to varying scientific and common names
- Inadequate product descriptions
- Complex industry-specific taxonomies for categorization
These challenges required ChemDirect to standardize a product database of over 500,000 items with incomplete data, ensuring a rich web and SEO experience for users.
Approach to the Solution
Using AI for Data Enrichment
To address these challenges, ChemDirect leveraged AI for data enrichment, integrating it into their data pipeline with Zeytech’s help. The approach included:
- Data Quality Detection: Utilizing AI to enhance data quality from secondary sources.
- Parsing Industry Specifications: Using AI to convert human-readable industry technical specifications from PDF format to machine-readable JSON.
- Enriching Chemical Synonyms: Leveraging a Large Language Model (LLM) with chemical knowledge to improve the accuracy of chemical synonyms.
- Product Description Enhancement: Using LLM to detect insufficient product descriptions and enrich them based on chemical knowledge and other attribute sets.
- Standardizing Industry Taxonomy: Creating a standard industry taxonomy and employing LLM chemical knowledge to assign products to appropriate categories in the taxonomy tree.
Use Case Highlights
Technologies Used
- Python
- Hybrid Cloud Data Pipeline
- Storage Infrastructure
- Private LLM Model and REST APIs
- CSV, JSON, PDF, Parquet files
- Terraform
Zeytech Services
- Technical Advising
- Solution Architecting
- AI Governance
- Security Thought Leadership
- Data Engineering
- Team Collaboration
Business Impacts
- Established AI-enriched pipeline
- Improved data quality
- Bridged external data sources
- Increased product searchability
- Enabled 500K+ item dataset to process in less than a week
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