Case Study - bravo background
Case Study - bravo background
Case Study - bravo logo

Bravo Talent Corporation is a technology start-up specializing in HR software and solutions. They offer smart talent management and HR software to automate and streamline the HR process, employee recruitment, onboarding, performance management, training and development, compensation, and benefits administration.

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Challenge

In order to make bravoSUITE a more brilliant platform for HR professionals, Bravo knew that they had to automate many phases in their system.

They found out that the manual résumé screening stage in the hiring cycle takes recruiters a lot of time and is highly prone to human errors. They wanted a tool integrated directly into its system to extract résumé data automatically.

To break through the challenges and stick to the plan, Kikora had no other way but to boost its force by outsourcing a dedicated team of AI engineers. And Neurond was the strategic AI partner Bravo chose to collaborate with.

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Solution

In response to the project requirements of Bravo, Neurond assembled a team of three AI Engineers. Our engineers quickly came up with a problem formulation for the résumé data extraction tool.

We combined techniques such as Computer Vision, Rule-based NLP, and Name Entity Recognition to auto-separate CV/resume data into fields and attributes. The output is structured resume data stored in JSON or XML for storage, reporting, & editing.

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Impact

After six months of working non-stop, we released the first version of the Dr.Parser that can extract data from CVs written in both English and Vietnamese. During the development of Dr.Parser, we kept improving the extraction speed and accuracy.

Bravo was pleased with the deliverable and impressed with our team’s performance regarding time management and high productivity, so the company decided to extend the contract with Neurond. The project is now ongoing, and it is in the second phase of development. The project’s next step will focus on the Smart Search function, allowing recruiters to find the most suitable candidate from the system’s database.

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