AI-powered resume parsing solution

Process Flow: The Visual Story

Technology Stack

Problem Statement


Time Consuming: Sifting through a large number of resumes is a time-consuming process and can consume a lot of resources and effort.


Inaccuracy: The manual process of matching resumes with job descriptions can be prone to human error and may result in an incorrect match.


Bias: The manual process of matching resumes can lead to unconscious biases, which can result in the exclusion of qualified candidates


Tedious Work: The manual process of matching resumes with job descriptions can be repetitive and boring, leading to burnout and decreased efficiency among recruiters.


Lack of Scalability: As the number of resumes increases, the manual process becomes more challenging and less scalable, making it difficult to keep up with the demand.

Solution Overview


Automated matching of resumes with job descriptions results in many benefits and significantly reduces the time spent reviewing resumes.


OptiSol has developed an AI-based Resume Ranker solution to aid talent acquisition in selecting the right candidates.


The solution handles unstructured data in resumes by building a pipeline to extract text.


The extracted text is input into the Universal Sentence Encoder, creating sentence embedding for both the resume and job description.


The similarity between the two documents is determined using cosine similarity.

Business Impact


Time Savings: Automated matching of resumes and job descriptions can save a lot of time and effort that would otherwise be spent on manually reviewing resumes.


Increased Accuracy: An AI based solution can help eliminate human error and provide a more accurate match between resumes and job descriptions, leading to better hiring decisions.


Increased Efficiency: The resume ranker solution can process a large number of resumes in a short amount of time, leading to a more efficient and streamlined recruitment process.


Improved Candidate Experience: With a more efficient and accurate matching process, candidates can receive quicker feedback on their applications, improving the overall candidate experience.


Better Resource Allocation: By freeing up time and resources, the use of an AI based resume ranker solution can allow talent acquisition teams to focus on other important tasks, such as candidate engagement and relationship building.

Testimonials of Our Happy Clients

Related Success Stories

Related Insights

The 5 Phases of Natural Language Processing

Natural language processing (NLP) is the interactions between computers and human language, how to program computers to process…

Automating Minutes of Meetings using Text Analytics

In the corporate world, we attend numerous meetings daily. We may have meetings that last longer than expected, and we may forget to take…

Text analytics is redefining the way attorneys do their job

As predicted by many successful executives, NLP market is expected to grow to a value of $16 billion by 2021, it is indeed no surprise to see the tech…

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






Projects for
reputed Clients


5 yrs

in AI & ML

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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.
Connect With Us!