Leading Tax Filing Company
Intelligent Document Processing
Problem
Key-value pair data generated by the internal OCR engine from tax forms had to be manually mapped to corresponding fields in the tax engine for downstream processing, causing delays and inefficiencies.
Solution
An NLP-powered auto-mapping engine was developed to automatically process uploaded documents, ensuring seamless integration with the tax engine and streamlining downstream operations.
Outcome
The auto-mapping engine demonstrated high accuracy, significantly reducing manual effort and greatly accelerating the tax filing process.


Automating Tax Form Mapping for Improved Efficiency
The client, a tax filing company, faced inefficiencies in processing tax forms. Their internal OCR engine returned key-value pairs that required manual mapping to the tax engine metadata, making the process slow and error-prone. To address this, an NLP-based auto-mapping engine was developed, leveraging string similarity and semantic analysis to automate the mapping process.



The auto-mapping engine achieved 99% accuracy, significantly reducing errors and improving efficiency. This led to a 40% reduction in processing time, delivering substantial time savings and enhancing overall operational performance.
Speeding up Taxes
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