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How are Logistics Companies Using AI to Extract data-Must know

Logistics Companies Using AI to Extract data

Document extraction automation for logistics companies

Logistics companies have a mind boggling tedious task to maintain a team for data entry to process receipts and logistics information such as shipment data to feed into ERP or TMS.

This costs the company a lot by employing people and even the process is very slow . Let’s introduce DocExtractor , a one stop solution to process large volumes of logistics paperwork information into structure data . Besides, it will be plugged to any ERP systems.

AI driven data extraction is the need of the hour as it basically cut the operational cost and is more efficient than a human driven process.

Also, Docextractor can reduce the data entry based cost by 75% for any company by leveraging AI driven data extraction.

Below are some of the use case where Docextractor can seamlessly fits in-

Extraction of Shipment Data

Most OCR algorithms does not work efficiently in extracting Address and pin codes efficiently. Besides, our state of the art deep learning algorithm fits in seamlessly to extract any particular data from Shipments.

automate data extraction in logistics companies with AI

Extraction of data from Shipping Invoice/Bills

Docextractor algorithm is battle tested to extract date, amount, total purchase, tax data address etc. from invoices which is not even template driven. Also, the data extracted will directly sync to ERP system using API.

The Docextractor API will integrate itself with mobile apps, which will bring efficiency with in the teams to collect data in real time.

Docextractor can handle huge volumes of data with great accuracy, even it can surpass human in some cases.

Besides, Docextractor can help you eliminate repetitive manual task, cost saving, time saving and mostly greater value creation for your team.

Wants to transform the data extraction journey using automation , feel free to get in touch with us

Schedule a call for a free consultation on Document Processing at scale.

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