HRTech - NLP Enabled Resume Builder for Recuiters Portal using Python

Business challenges

Time-consuming process: Manually creating and formatting resumes can be time-consuming, especially when dealing with a high volume of applicants.

Error-prone: Manually inputting data into a template can lead to errors and inconsistencies, which can impact the credibility of the HR department.

Lack of customization: With manual formatting, there is limited flexibility to customize resumes based on specific job requirements or company branding.

Inefficient use of resources: When HR professionals are spending significant time formatting resumes, it takes away from other important tasks, such as sourcing and evaluating candidates.

Inconsistent formatting: Without a standardized template, there can be inconsistencies in the formatting and presentation of resumes, making it difficult to compare and evaluate candidates.

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Solution Overview

We worked with a startup firm in developing a Resume builder solution that automates the entire process from resume loading to dynamic template fitting.

Resumes are unstructured data with different file types, initially, they will be converted into an HTML file to get a raw text.

Custom data is prepared, For example, the objective, overview, and summary remain the same but differs from resume to resume. The Data set is prepared to map these similar fields into the same bucket.

Trained a machine learning model to classify the fields in the resumes.

Built a NER model to extract granular level details like start date, end date, location, etc.

WEB UI is developed where a resume is dropped, formatted resume can be downloaded.

Business Impact

Increased Efficiency: Automation reduces the manual effort and time required to build a resume, allowing recruiters, and hiring managers to focus on more strategic tasks.

Improved Consistency: Automated resume builders help ensure that all resumes have a consistent format, style, and tone, improving the professional appearance and readability of the resumes.

Enhanced Customization: The dynamic template fitting feature allows the resume builder to automatically adjust the format, style, and layout of the resume based on the information entered, providing a more customized and polished presentation.

Reduced Errors: Automated resume builders reduce the risk of human error in the resume building process, such as typos or incorrect formatting.

Increased Productivity: Automated resume builders can streamline the resume building process, allowing recruiters, and hiring managers to process more resumes in less time, ultimately increasing productivity.

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Technology Stack

Trusted and Proven Engagement Model

A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
Our NDA-driven process is established to keep clients’ data and IP safe and secure.

The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
This helps clarify the goals, and limitations, and deliver quality products & services.

Our engagement model defines the project size, project development plan, duration, concept, POC etc.
Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).

The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.

On signing the SOW, an official project kick-off meeting shall be initiated.
Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

5yrs

in AI & ML
Engineering

40+

AI & ML
Projects for
reputed Clients

50+

AI & ML
Engineers

Awarded as Winner among 1000 contestants at TechSHack Hackathon