Case Study
Laigo.ai
Empowering companies big and small
Overview:
Laigo is a software company that focuses on developing Deep Learning Models for Natural Language Processing (NLP). With their expertise in intelligent document processing (IDP), they offer a range of services that cater to various use cases, including information extraction and document classification.
Laigo’s NLP models use state-of-the-art technology to accurately process natural language data and extract meaningful insights from it. Their IDP solutions are designed to automate manual document processing tasks, saving time and improving efficiency for businesses. With their cutting-edge technology and specialized focus on IDP, Laigo is positioned to help organizations across industries achieve their document processing goals.
Problem:
Laigo’s key responsibility in their working process is to continually train Deep Learning models for NLP.
However, their data annotation process either involved manual labeling or open source tools, which demanded a significant amount of human effort and time. As a result, completing the project within the given timeline became a challenge.
Solution:
To tackle Laigo’s issue, they opted for a cost-effective solution, which involved implementing a data labeling tool such as UBIAI. By integrating UBIAI tools, they were able to address various NLP use cases simultaneously instead of restricting themselves to a single one.
The automation of their data labeling process allowed them to accomplish their objectives quicker than previously.
Consequently, they avoided the need to switch between different tools for different use cases, saving them both money and time.
After Using UBIAI:
Following the integration of UBIAI tools, Laigo could resolve a greater number of business use cases within the NLP domain and concentrate on addressing more intricate issues, free from the laborious and tiring data labeling process.
According to a data scientist at Laigo, “UBIAI is the ultimate solution for NLP data labeling. The company has put in significant efforts to expand and develop novel methodologies to automate the data labeling process.”
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