AI-Powered Enrichment and Custom Data Scraping for eCommerce Seller: Achieving 99.8% Accuracy and 60% Faster Time-to-Market

99.8%

data accuracy achieved

60%

faster time-to-market

98%

data consistency score

Service

  • Product Data Scraping Services
  • Data Enrichment Services
  • Product Data Classification

Platform

  • MS Excel
  • MS SQL

The Client

A Leading eCommerce Distributor of MRO Supplies and Industrial Machinery

Our client operates as a premier B2B distributor specializing in industrial supplies, manufacturing equipment, and maintenance solutions. With a diverse customer base spanning construction, manufacturing, and facilities management sectors, they maintain strategic partnerships with over 6,000 brand manufacturers globally.

Project Requirements

Streamlining Product Data Extraction, Enrichment, and Standardization Across 2.5 Million SKUs

The client required our product data scraping and enrichment services to improve data accuracy and integrity across its 2.5 million SKUs. The project scope included:

The project scope included:

  • Product Data Scraping at Scale: Extract detailed product information from over 450 manufacturer and supplier websites, capturing specifications, pricing, availability, images, technical documents, and compliance certifications. The extraction needed to accommodate diverse website architectures spanning multiple eCommerce platforms.
  • AI-powered Data Enrichment: Address substantial data gaps within their existing product database by leveraging automated data enrichment strategies. We were required to deploy advanced prompt engineering techniques using ChatGPT to systematically identify and append missing details across critical product attributes, including technical specifications, application use cases, compatibility information, and industry certifications.
  • Product Taxonomy Development and Categorization: Implement a comprehensive product classification system aligned with industry-standard taxonomies (UNSPSC codes) to improve navigation and user experience for the client’s website.
  • Product Data Matching and Validation: Accurately match and validate product data for new and updated SKUs monthly against existing catalog entries, ensuring 98%+ data accuracy. Identify and consolidate duplicate listings across different supplier nomenclatures to maintain a reliable database.

Project Challenges

Meeting Data Quality Benchmarks in AI-Driven Scraping and Enrichment

The project presented several interconnected challenges that required innovative problem-solving approaches:

Anti-Scraping Mechanisms

Varying Website Architectures and Anti-Scraping Mechanisms

The client wanted to scrape product data from 450+ manufacturer websites built on varied platforms—Magento, BigCommerce, custom PHP frameworks, WooCommerce, and proprietary systems. Each website had a distinct backend, anti-scraping mechanisms, rate limiting, and data presentation formats. We needed to develop custom scripts to efficiently scrape data from these complex, protected web sources.

Scalable Automation

Need for Scalable Automation

We were required to continuously extract and enrich data for approximately 60,000 SKUs each month, with weekly updates to pricing and inventory details. Manual scripting approaches couldn't achieve the required throughput, necessitating sophisticated automation frameworks that could adapt to site changes without constant developer intervention.

Data Standardization

Data Standardization and Quality Issues

The technical attributes in industrial product data were presented inconsistently: some in metric units, others in imperial units, and some using industry abbreviations or full terminology. Additional post-processing was required to convert and standardize this data while preserving technical accuracy for a unified database.

Data Accuracy

Data Accuracy Concerns with AI-Driven Enrichment Workflow

While ChatGPT is proven to be time-efficient for large-scale data cleansing, enrichment, and categorization, it has its own limitations—the most significant being data quality. For instance, some of the UNSPSC codes generated by ChatGPT were outdated. To ensure accuracy, manual intervention was required at every stage along with automated workflows.

Our Solution

Deploying a Human-Supervised Automation Framework for Enterprise-Scale Data Extraction and Enrichment

We designed a scalable workflow that strategically balanced automation efficiency with human expertise to ensure data quality and project success. A team of 8 data specialists (including web scraping experts, QA specialists, prompt engineer, and subject matter experts) was deployed to work dedicatedly on this project, ensuring quick turnaround. Our approach involved:

Multi-Platform Data Extraction Through Custom Scripts

The list of web sources was provided to us in batches for product data scraping. Also, to maintain compliance, the client shared detailed guidelines on what data to scrape and what to exclude. Our web scraping experts created Python-based custom scripts (to automate HTTP requests) and leveraged reliable third-party data extraction tools & API to streamline large-scale scraping. We implemented:

  • Headless browser automation for JavaScript-rendered content
  • Intelligent request throttling and rotating proxy pools to circumvent rate limits
  • Custom parsers designed to adapt to each manufacturer's specific HTML structure and CSS selectors
  • Curl requests to efficiently bypass web interface restrictions and retrieve data
  • BeautifulSoup Objects for intelligent HTML parsing, navigating complex DOM structures, and extracting specified datafields

ChatGPT-Powered Data Enrichment

We created a customized ChatGPT solution for data enrichment, leveraging ChatGPT-4's advanced capabilities. Our prompt engineer designed a comprehensive library of specialized prompts with precise instructions that guided the AI toward accurate, contextually relevant outputs for diverse product categories and data enrichment scenarios.

Along with training the custom GPT solution, we ensured data accuracy and quality in the enrichment and cleansing process through:

  • Multi-step validation where AI-provided enriched data were cross-referenced against manufacturer websites and industry databases
  • Human review queues for high-value or complex products where AI confidence scores fell below defined thresholds
  • Iterative prompt refinement based on error pattern analysis

Taxonomy Development & UNSPSC Categorization

Our taxonomy specialist worked closely with the client to develop a hierarchical data classification framework that aligns with their product line. We leveraged both manual intervention and ChatGPT to assign accurate UNSPSC codes:

  • Utilized ChatGPT to analyze specific technical attributes (material, dimensions, and tolerance levels) to automatically suggest the most relevant UNSPSC code, reducing manual classification time and efforts
  • Assigned accurate UNSPSC codes for every product by cross-referencing the UNSPSC website
  • Developed a master mapping sheet that linked the newly assigned UNSPSC codes to the client’s legacy codes, ensuring zero data loss during the migration to the new digital marketplace.

Human-Led Quality Control and Data Processing

To ensure 98%+ accuracy and eliminate inconsistencies, errors, and duplicates from the client’s catalog, we established a multi-layer QA & data processing process, led by subject matter experts:

  • Automated data validation scripts checking for completeness, format consistency, and logical constraints
  • Sample-based manual audits of enriched records
  • Standardized inconsistent units of measurement (e.g., converting "in.", "inch", and "''" to a uniform format) and normalized brand naming conventions
  • Created a structured workflow for "ambiguous data," where flagged records were manually validated and enriched by subject matter experts
  • Used fuzzy-logic algorithms to identify and eliminate redundant entries, ensuring each unique industrial part had a single, optimized "Golden Record.

Project Outcomes

Leveraging automation with human supervision, we not only successfully scraped, enriched, and validated 2.5Mn SKUs in the client’s provided timeframe but also helped them 60% faster time-to-market.

Here is an outcome summary at a glance:

Metric Pre-Project State Post-Project State
Data Accuracy 80% (several product listings had errors or outdated details) 99.8% (all listings had enriched and validated data)
Product Data Refresh Rate Product data was updated manually every 2-3 weeks Real-time, automated updates to prices and availability
SKU Handling Capacity Limited to handling 1 million SKUs with manual processes Scalable solution supporting 2.5Mn+ million SKUs
Time Spent on Data Management 30+ hours per week spent manually updating product data 60% reduction in time spent with automated workflows
Product Categorization Accuracy 74% (inconsistent taxonomies, poor search relevance) 99% (standardized UNSPSC codes + custom hierarchies)
Data Consistency Score 61% (format inconsistencies across sources) 98% (standardized formatting and validation)

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