Overview
A leading HVAC and boiler systems manufacturer needed a reliable, structured data source for product dimensions - including height, width, and depth - which were previously scattered across multiple manufacturer websites and PDFs.
Manual data extraction was slow, error-prone, and unsustainable, impacting the company’s ability to perform quick product analysis, comparisons, and operational reporting.
Manual data extraction was slow, error-prone, and unsustainable, impacting the company’s ability to perform quick product analysis, comparisons, and operational reporting.
- Business Challenge
- Project Objectives
The client’s product dimension data was fragmented across disconnected repositories, making it difficult to maintain accuracy and consistency.
Key challenges included:
- Dimensions spread across PDFs, web pages, and third-party databases
- Manual extraction leading to inefficiencies and errors
- Lack of centralized, structured data for cross-manufacturer comparison and reporting
Aquarient Technologies built a DataOps-based automation pipeline to extract, validate, and structure product dimension data efficiently and accurately.
Solution Highlights:
- Web Scraping Automation: Extracted key product parameters – height, width, depth, and weight from manufacturer websites, PDFs, and third-party sources.
- Data Structuring: Organized technical specifications, model numbers, and dimensions into Excel-based searchable datasets.
- Validation & Standardization: Applied rule-based validation and consistent formatting to ensure high-quality, reliable data.
- Centralized Repository: Created a single point of truth for all verified product dimension data.
Why It Matters
This project transformed how the client manages technical data turning hours of manual effort into an automated, repeatable process.
The impact included:
- Faster technical support and procurement decisions
- Reliable and consistent product analysis across brands
- Operational efficiency through structured, validated data
Results & Business Impact
| Outcome | Impact |
|---|---|
| Automated Data Collection | Replaced manual extraction with a fully automated pipeline |
| Improved Accuracy | Eliminated human errors via validation logic and standardization |
| Centralized Repository | One structured database for all product dimensions and specifications |
| Time Savings | Reduced data collection effort by over 70% |
Technologies Used
- Python (Requests, BeautifulSoup, Pandas)
- PyPDF2 / pdfplumber (for PDF extraction)
- Excel-based data validation and formatting logic
- Automated scheduling scripts for ongoing refreshes
