Client Success Story
Client Profile
An eCommerce reseller founded in the early 1980s has grown into a trusted reseller for industrial machinery, hardware supplies, and household appliances. The business maintains active vendor relationships with more than 7,000 manufacturer brands and operates two dedicated eCommerce storefronts serving a broad range of sectors, including plumbing, electrical, and equipment supply. With a catalog exceeding 2 million products, maintaining consistent and accurate product data at scale has been an operational priority.
Project Scope
The scope of work included three interconnected product data management functions for the client's 2M+ SKU catalog:
The team was tasked with extracting product data from multiple third-party brand websites shared by the client. This process required precision to preserve all available attributes, followed by removing special characters and format standardization to ensure the data could be integrated directly into pre-approved templates.
The client's platform had no existing category structure or classification framework. The team was responsible for designing a taxonomy from scratch to match the client's product range and applying accurate UNSPSC codes to each item to improve platform navigation and searchability.
A significant volume of scraped records contained incomplete or missing data fields. The client requested the use of AI — specifically ChatGPT — to fill these gaps systematically through customized prompting, ensuring each enriched field met defined quality and consistency standards.
Project Challenges
Each phase of this engagement brought distinct technical and operational challenges that required targeted responses at every stage. The key obstacles included:
Each brand website presented its own scraping constraints—unique HTML structures, embedded content restrictions, and varying anti-bot settings. There was no single-script solution; the team had to build and deploy distinct extraction tools per platform without compromising delivery timelines.
Scripts built for targeted, single-site extraction lacked the throughput needed for simultaneous, high-volume collection across multiple brand websites. As batch sizes grew, the performance constraints of manually developed scripts became a limiting factor that required proactive management.
Much of the scraped data arrived without proper categorization or consistent formatting. Extensive post-processing was required to remove non-standard characters, reformat fields, and align all records with the client's templates—steps that added time and complexity to an already demanding pipeline.
ChatGPT occasionally returned outdated UNSPSC codes or produced category assignments that were inaccurate. Managing AI output quality required precise and specific prompts and systematic human review at each enrichment and categorization step to ensure results met acceptable accuracy standards.
Our Approach
A six-person team was assembled to manage the engagement. The team included a prompt engineer, a data scraping specialist, and a dedicated QA resource.
Brand websites for scraping were delivered to the team in batches. Custom Python scripts and extraction tools were developed for each site to collect product attributes—including descriptions, pricing, taxonomy, reviews, and product categories—at scale and with high accuracy.
Post-product data scraping, any special characters were removed, and data were organized according to the client's structural requirements. Three approaches applied across scraping operations:
The client established clear guidelines on permissible data scraping targets, ensuring all scraping activity remained compliant with ethical and legal standards while minimizing the risk of website blocking.
To establish a consistent product classification structure, we developed a custom taxonomy aligned with the client’s product range, drawing from Google’s category framework. We then used ChatGPT to suggest the closest category matches and cross-referenced each product against the UNSPSC directory to assign the correct codes.
Our team manually reviewed every category and code to remove inaccuracies and ensure the final classification remained precise and reliable.
A custom GPT-4 integration was developed after purchasing API tokens to automate the enrichment of incomplete product data. Prompt engineers developed highly specific instructions to guide the model in filling missing fields — including weights, descriptions, and category assignments — with contextually accurate outputs.
We also used our master database to enrich the records further, adding relevant information that improved overall data completeness and quality.
Automation Approach
Automation handled most of the work across scraping, enrichment, categorization, and cleansing processes. Human oversight was applied at each stage to validate outputs, identify errors, and ensure every record met the client’s quality standards before delivery.
| Task | Automation | Human Intervention |
|---|---|---|
| Data Scraping | Python scripts and scraping tools automated the retrieval of product data across all brand websites. | Manual review was conducted to verify accuracy, remove unwanted special characters, and confirm the data matched client formatting standards. |
| Data Enrichment | ChatGPT was used to identify and fill missing product information, including descriptions, weights, and categories. | Team members validated AI-generated entries and cross-referenced the in-house master database to close any remaining data gaps. |
| Product Categorization | ChatGPT generated initial category suggestions and proposed UNSPSC code assignments for all products. | Resources mapped every code using the UNSPSC reference portal and corrected any outdated or ambiguous classifications. |
Results Delivered
A fully structured product taxonomy was built and deployed. Team4eCom eliminated categorization gaps that previously affected product discovery and made browsing less intuitive.
Error-free product data was delivered through a combination of automated cleansing workflows and strict manual quality checks at every stage.
Operational efficiency improved through automation across data scraping, enrichment, categorization, and standardization, reducing manual effort and improving delivery timelines.
Project Workflow
The following outlines how each stage of the engagement was executed, from initial onboarding through final delivery:
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Team4eCom delivers end-to-end product data management through a structured model, where automation handles high-volume processing and human review safeguards accuracy, helping businesses manage large product volumes with greater efficiency and confidence.
Reach out at info@team4ecom.com to discuss your requirements.