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Automated text classification and validation of thousands of news articles enhances performance of AI-model for German construction technology company Banner

Client Profile

A German technology company that leverages its in-house construction leads data platform to disseminate exhaustive and well-researched construction projects data across USA and Europe. Its clients include small to Fortune 500 companies operating in the real estate, construction, and building materials manufacturing space.

Business Need .

The company captured upcoming or existing construction projects data in real-time from multi-lingual and multi-format online publications across Europe and USA. The unstructured data parsed using automated crawlers included property type, project start/end date, site location, size, cost, and construction phases. The captured data was auto-classified into relevant segments using intelligent AI algorithms. 20% of the data with high complexity which could not be auto-tagged was labelled using human intervention.

The company partnered with Hitech to:

  • verify and validate the auto classified text to ensure accuracy and navigate the challenges of tool performance
  • append missing information
  • manually annotate the 20% data which couldn’t be auto classified

Challenges .

Solution .

Hitech’s data specialists classified and labelled more than 10,000 construction related articles. The labelled data was then funneled through a validation, verification and data append process to ensure accuracy and credibility of annotation.

The verified and validated data improved the accuracy of the AI algorithms and ensured better and more relevant search results on construction project data.

Approach .

Technology Used:

Secure Login to Client portal through web-browser

Business Impact .

Enhanced text labelling accuracy increased performance of the model
Significant reduction in turnaround time from days to few hours
Increased algorithmic accuracy resulted in higher customer acquisitio
Offshoring model saved 50% on project cost for client
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