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Traditional resume screening methods like FIFO and ATS filters can be inefficient and prone to errors, leading to missed opportunities with great candidates. Brainner’s AI-powered approach offers a more efficient, high-quality solution by prioritizing candidates based on targeted criteria, ensuring 100% of resumes are screened consistently while minimizing manual review.

The method you choose for resume screening can dramatically impact both the quality and efficiency of your hiring process. Traditional methods like First In, First Out (FIFO) or ATS filters can be time-consuming, inefficient, and prone to missing great candidates. With Brainner’s AI-powered approach, you can prioritize resumes in a way that guarantees both high-quality and efficient analysis while minimizing risks.

Candidates are reviewed in the order they apply. This method is arbitrary, inefficient, and carries a high risk of missing qualified candidates or reaching them too late. The quality of analysis also suffers due to inconsistency from human reviewers, who may become fatigued over time. This inefficiency is driven by a heavily manual process, with around 80-90% of resumes reviewed manually. The risk of overlooking candidates is high because the best candidate might apply at the last minute, and you either won’t have time to analyze them, or you may reach them a week too late.

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This method filters candidates based on specific variables extracted from the ATS (e.g., skills or expected salary). While somewhat helpful, ATS filters rely on basic keyword matching, which can be rigid, error-prone, and limited in scope—it doesn’t provide a holistic analysis of all criteria. Qualified candidates can be easily overlooked if their resumes don’t use the exact keywords. Additionally, since recruiters don’t fully trust this method, they end up manually reviewing 50-60% of resumes, which remains a tedious and inefficient task. The accuracy of ATS filters is often lacking because they don’t offer the depth of understanding provided by AI-powered semantic search.

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The Brainner Approach: A Better Starting Point for Analysis

Brainner’s AI analyzes, in real time, whether candidates meet the recruiting criteria defined by the recruiter. It assigns a score to each candidate, with 80% weight given to mandatory criteria and 20% to preferred ones. This score is simply a tool to sort candidates by how many criteria they meet—it doesn’t mean the candidate at the top of the list is necessarily the “best” candidate.

Brainner provides a prioritized starting point

. This allows you to analyze resumes more efficiently, focusing first on candidates who meet the most criteria. Instead of jumping between promising and irrelevant resumes, you’ll review a steady flow of well-matched candidates—and may even 

skip the bottom 90% of less qualified candidates entirely.

100% of resumes are screened with the same level of accuracy and consistency

. It’s a targeted method that reduces the risk of overlooking great candidates. Thanks to the precision of AI, 

only 5-10% of resumes require manual review

. Additionally, Brainner converts resume screening into actionable data, offering insights like where your best candidates are coming from.

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Brainner’s AI-powered system offers a low-risk, high-quality analysis that ensures you don’t miss exceptional candidates. It’s precise, efficient, and keeps your focus on a pool of well-matched candidates. By spending less time sifting through resumes, you can devote more time to meaningful interviews and informed hiring decisions.

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How to Prioritize Resume Screening: The Best Starting Point

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Semantic search is transforming resume screening by understanding the context and meaning behind words, unlike outdated keyword matching. Tools like Brainner leverage this technology to provide more accurate, bias-free candidate analysis, ensuring recruiters find the right talent faster and more effectively.

Introduction: The Evolution of Resume Screening Softwares

For years, talent acquisition teams have struggled with the limitations of keyword matching in resume screening. ATS (Applicant Tracking Systems) often relied on simple keyword matching techniques to filter resumes, but these methods were far from perfect. Great candidates were being overlooked, and many recruiters stopped trusting the systems, resorting to time-consuming manual reviews.

Fast forward to today, and the rise of AI-powered semantic search is changing the game. This new approach has allowed recruiters to find more relevant candidates by understanding the context of their experiences, rather than just matching keywords.

Keyword Matching: The Old Way of Screening

Keyword matching is a straightforward process that simply scans resumes for specific words or phrases that match the job description. If the resume contains the right keywords, the system marks the candidate as a potential fit. If not, they’re filtered out.

However, keyword matching has its limitations:

Semantic Search: A Smarter Way to Find Talent

Semantic search is an advanced method that goes beyond matching keywords. It understands the context and meaning of the words in a resume and job description. Instead of just looking for exact keyword matches, it analyzes the relationships between terms, making it much more effective at identifying qualified candidates.

In the past, implementing semantic search was a complex and technical process that required specialized knowledge. However, modern AI platforms like Brainner have made this technology accessible to everyone.

Brainner’s resume screening software uses semantic search to analyze the criteria defined by recruiters. You don’t need to rely on rigid keywords anymore—Brainner can interpret the context of each resume and determine whether candidates meet specific criteria such as “years of experience in a specific technology,” “education degrees,” or even “job rotation patterns.”

What sets Brainner apart is that it not only performs this analysis accurately but also helps to minimize bias. Since the system is looking purely at whether a candidate meets the criteria, it removes any potential bias related to the candidate’s name, gender, or background. Ultimately, Brainner presents the data in a way that empowers recruiters to make informed decisions based on the candidate’s qualifications.

Conclusion: A Fairer, More Effective Screening Process

Semantic search is revolutionizing how resumes are screened, offering recruiters a powerful tool to find the right talent without the limitations of keyword matching. By going beyond exact word matches and understanding the context of a candidate’s experience, tools like Brainner make the hiring process faster, fairer, and more effective.

As AI continues to evolve, semantic search will undoubtedly play a larger role in helping recruiters find the best candidates while reducing bias and manual work. For teams looking to streamline their hiring process, switching from keyword matching to semantic search is the next logical step.

The Benefits of Semantic Search Over Keyword Matching in Resume Screening

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Defining clear and aligned criteria for resume screening is crucial for an efficient and fair recruitment process. By combining human expertise with Brainner’s AI, recruiters can reduce bias, save time, and focus on top candidates who meet their exact requirements. Find a guide to best practices in our full article to help you optimize your screening process.

In today’s fast-paced hiring environment, defining resume screening criteria is more than just a step in the process—it’s the foundation of effective recruitment. As hiring volumes increase, and more applications flood in, creating clear and aligned criteria helps recruiters sift through resumes quickly and accurately. Without well-defined criteria, you risk wasting time on unqualified candidates or, worse, missing out on top talent.

A survey shows that up to 85% of resumes submitted for job openings are irrelevant to the position. This highlights the need for a well-planned and targeted approach when setting criteria. Working closely with your hiring team to define the right criteria from the start will save time, ensure better candidate matches, and improve overall hiring efficiency.

Brainner’s Approach to Criteria Definition

At Brainner, defining screening criteria is a critical part of our process. Our AI model was built to follow your instructions precisely, ensuring that it only highlights candidates that meet your specific requirements. This approach not only helps reduce bias but also ensures that human judgment is central to defining what matters. Our AI model steps in to handle the repetitive work, but humans stay in control of the key decisions.

This balance allows for faster and fairer resume screening. By clearly defining the criteria, recruiters set the foundation, and AI does the heavy lifting by analyzing resumes impartially, following the guidelines exactly. This helps remove unconscious bias from the process while ensuring the criteria align with your hiring goals.

Best Practices for Defining Resume Screening Criteria

We collaborate closely with our clients to help them define their screening criteria effectively during the onboarding process. Each new role presents its own challenges, and we constantly learn and improve our best practices. Here are some key takeaways:

1. The more specific the criteria, the more accurate the results.

Being detailed and clear makes it easier for Brainner to screen resumes accurately. Vague criteria can lead to confusion and inconsistency in the results.

2. Write criteria that answer the question: “What should the candidate meet?”

3. Avoid abbreviations when writing criteria.

Abbreviations can cause confusion. Always spell things out for clarity.

4. Quantify work experience and skills for accuracy.

By quantifying, you ensure the AI differentiates between junior and senior candidates.

5. Use Boolean logic (AND, OR, NOT) for complex criteria.

This ensures that candidates meet the exact combinations of skills and experience you’re looking for.

6. Include relevant criteria beyond the job description.

There are often additional qualities or experiences that are important for the role but aren’t listed in the job description.

7. Consider the frequency of job changes as a criterion.

While some job changes may be beyond the candidate’s control, it’s still a useful indicator for stability.

8. Decide whether to consider soft skills.

Although it can be hard to evaluate soft skills from a resume, Brainner’s AI can offer an initial assessment based on the content. Some users choose to use this data to complement their decision-making process.

Defining criteria for resume screening is a key task that needs careful planning and alignment with your hiring goals. Brainner’s AI-driven resume screening, combined with well-defined criteria, helps reduce bias, improve efficiency, and ensure that recruiters focus on the right candidates. By working closely with our clients, we continue to refine and perfect the process to help recruiters make better decisions, faster.

The Importance of Defining Resume Screening Criteria: A Key Task for Recruiters

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The article explores a fair and efficient approach to resume screening by combining AI and human expertise. It details a three-step process where recruiters define criteria, AI automates the screening of resumes against these criteria, and recruiters make the final decisions based on clear, explainable data. This method ensures a balanced, unbiased, and effective hiring process.

Analyzing resumes with AI has garnered a negative reputation in the industry due to the misuse of black-box algorithms. These systems often produce a magic score without any explanation, leaving recruiters clueless about how the scores are calculated, rendering them useless and unfair to candidates. Talent professionals are left wondering: 

How can we use a parameter that we don’t understand? How can we explain that number to users from the team? Are we being fair with candidates? How can we manage the biases that AI models carry from their training data? How can we mitigate potential AI hallucination?

Brainner’s approach to resume screening with AI was created in collaboration with senior recruiters and talent acquisition managers. 

The premise is to use AI for tasks where it excels in productivity and impartiality while prioritizing humans in those activities where their knowledge, sensitivity, and experience are critical

. This is a three-step approach where recruiters are heavily involved at the beginning for job criteria definition and calibration, and at the end of the process, analyzing the processed information and making decisions. Meanwhile, AI handles the processing of recruiters’ instructions, analyzing resumes, and providing information for decision-making.

Part 1: Job Criteria Definition and Calibration - Human Activity

After talent acquisition teams and hiring managers agree on the job description and requirements, recruiters must select the most important criteria (e.g., working experience, skills, education) that will be used for screening resumes.. The goal is  to replicate the key criteria they would use to manually screen resumes. After that, they should prioritise these requirements by defining which ones are mandatory versus preferred and provide weights (e.g., high, medium, low importance) 

By involving human recruiters in this step, we ensure the search targets exactly what the company needs, leveraging the recruiters’ sensitivity and expertise to deliver high-quality and well-targeted inputs to the system.

To make the process more seamless, the Brainner platform integrates with top ATS in the market and generates an initial draft of screening criteria by importing the job description already uploaded to the ATS. This draft serves as the starting point for recruiters to customize by adding, modifying, or deleting criteria, and also classifying those as preferred or mandatory.

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		Image 1: Criteria definition for a back-end developer role

Once the criteria are defined and calibrated, it’s time to use Brainner’s AI to automate the tedious task of going through each resume to check if they meet all the requirements. Instead of a magic score, this approach breaks down the problem into parts, using AI to examine each resume against each specific requirement, and determine if the candidate fully meets those criteria, along with a brief explanation.. We trained our AI models to assess if candidates meet objective criteria (e.g., years of experience in a specific role, a bachelor’s degree in a specific field, or experience in specific tech skills).

The system then displays the information in a visual dynamic table, sorting candidates by a predictable score, calculated by weighting mandatory criteria more heavily than preferred ones and considering priorities among the criteria.

By involving AI in this step, we ensure that AI is used in a discipline where it excels: extracting information, analyzing it, and making conclusions about objective matters. Additionally, we relieve recruiters of a manual task that adds no value and can be automated.

Brainner’s integrations with ATS allow this process to be not only automatic, but also in real time. As candidates apply through the ATS job posting, resumes will be imported into Brainner and the analysis will be done in real time.

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Image 2: Candidate analyses, including a color coding system to indicate whether the candidate meets each criterion, justifications for each criterion, and a scoring system

Part 3: Candidate Selection for Interview Stages - Human Activity

Once the candidates have been analyzed, recruiters re-enter the process to review the output and select the best candidates. It’s important to note that in this approach, Brainner’s AI doesn’t  choose the best candidates outright; instead, we provide recruiters with the tools to filter out those who do not meet the most critical requirements and focus on the top 5-10% who do. Recruiters can then easily navigate this shortlist and manually review the top applicants' resumes, deciding 10x faster which candidates to interview for the role.

By involving human recruiters in this step, we ensure that the final decision is made by humans and not by AI, and we add a final manual check of top resumes.

To offer a more streamlined experience, decisions such as advancing candidates (indicating which stage) or archiving candidates (indicating reasons) can be made directly from Brainner, individually or in bulk, and these changes will be reflected in the ATS to continue the hiring process there.

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Image 3: Detailed report for each candidate

Brainner’s approach aims to enhance the recruiter’s job, shifting from a completely manual role that involves hours of reading resumes to a more strategic one where they spend less time on the activity and focus more on criteria definition, calibration, and data analysis. Additionally, we leverage top technologies only to replace manual tasks that can be automated and avoiding subjective decisions that are likely to be biassed. Recruiters who can adapt to this methodology, integrating new technologies into their current processes, will outperform their competition.

The Fairest Approach to Resume Screening with AI

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In today’s competitive job market, effective resume screening is essential. This article provides best practices for defining objective screening criteria and introduces Brainner, an AI-driven tool that automates the process based on precise recruiter-defined criteria, ensuring efficient and accurate candidate evaluation.

Nowadays, with platforms like LinkedIn facilitating job applications, companies are receiving up to 600-800 candidates per job listing. It’s a staggering number that can make resume screening feel like searching for a needle in a haystack. Implementing best practices in resume screening is crucial to streamline this process and identify the most suitable candidates efficiently.

When you’re sifting through this mountain of resumes, it’s easy to get sidetracked by subjective and biased conclusions. It’s important to acknowledge that this is a preliminary filter, where we are only evaluating specific criteria.

Instead, focus on objective criteria that we can determine through resume screening:

To make this process even more effective, try creating a scoreboard. Assign weights to each requirement and calculate scores for the candidates. This way, you have a clear, objective method to decide who progresses to the interview stage. It’s also important to distinguish between mandatory and preferred criteria—mandatory being the deal-breakers that disqualify a candidate if unmet.

Introducing Brainner: 10x Faster AI Resume Screening

Let’s be honest, resume screening can be a real time-drain. But there are tools out there that can automate this process and save your talent team hundreds of hours. Brainner is one such tool—an AI-driven resume screening software that follows your criteria to the letter. This helps ensure the screening process is both objective and efficient, cutting down your HR team’s workload significantly.

Brainner’s AI model is only as good as the criteria you feed it. Remember the saying, “junk in, junk out”? If your input criteria are vague or poorly defined, you’re not going to get great results. That’s why it’s essential to carefully customize and refine your criteria to get the best outcomes from our software.

Brainner’s Approach to Customizing Criteria

Once you upload your job description (JD), Brainner will extract an initial draft of recruiting criteria. But this is just the starting point. It’s your job to fine-tune these criteria by adding, deleting, and modifying them.

Finally, be strict in defining mandatory criteria. These are the deal-breakers—if the candidate doesn’t meet these, they should not be considered for an interview.

And that’s it; you’re now ready to start hiring smarter with Brainner. By customizing your screening criteria, you enhance the accuracy and efficiency of your hiring process, ensuring you select the best candidates based on objective measures.

What to look for in resume screening: Best practices

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When considering HR software, it’s essential to evaluate the return on investment (ROI). Brainner’s resume screening software proves to be a highly efficient tool, delivering significant time savings and financial returns. Brainner users report saving up to 90% of initial screening time. This substantial efficiency gain translates to an impressive ROI of up to 10x, making Brainner an invaluable asset in the recruitment process. By reducing manual tasks and accelerating the time-to-hire, Brainner helps recruiters focus on strategic activities and secure top talent more effectively.

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10x ROI Resume Screening Software

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In today’s competitive job market, integrating Applicant Tracking Systems (ATS) like Lever, Ashby, Greenhouse, and Workable with other technology platforms can significantly enhance the hiring process. Key integrations with tools such as Calendly, DocuSign, Brainner, and Checkr streamline recruitment, making it more efficient and candidate-friendly. By leveraging these integrations, companies can reduce administrative overhead, improve the candidate experience, and secure top talent more effectively.

Top 5 ATS Integrations to Supercharge Your Hiring Process

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Artificial intelligence (AI) is transforming tech recruitment by streamlining tasks such as writing job descriptions, sourcing candidates, and screening resumes, significantly increasing efficiency. While AI reduces unconscious bias and enhances the candidate experience, it works best in tandem with human recruiters who bring expertise and contextual understanding. This balanced approach allows organizations to attract top talent and make smarter hiring decisions.

Resume Screening

How to Prioritize Resume Screening: The Best Starting Point

The Benefits of Semantic Search Over Keyword Matching in Resume Screening

The Importance of Defining Resume Screening Criteria: A Key Task for Recruiters

The Fairest Approach to Resume Screening with AI

What to look for in resume screening: Best practices

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