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1. Upload résumés 2. We use AI to extract data 3. Download results as Excel, JSON or XML
Free Online Résumé Parser, by Affinda.
We put our solution head to head with a market leading CV parsing API provider
… and we won:
Our proof of concept, on launch, outperformed a global market leader. Since then, we’ve made a range of meaningful improvements to our free CV parser – to deliver truly best-in-class performance.
The best part is, our integrated paid solution is half the price of many competitors.
Save time, and get better recruitment outcomes.
Our résumé parsing tool is used to:
Populate candidate profiles
Screen candidates
Search an applicant database
Integrate AI functionality into your apps and workflows. Get your free API key, and add automated intelligence to your hiring pipeline.
Capable of extracting over 100 fields per résumé.
Affinda’s machine-learning solution can be used to extract more than 100 fields from each résumé, organizing them into searchable file formats. Fields being extracted include:
Personal details
Name, contact details, phone, email, websites, and more
Work history
Employer, job title, location, dates employed
Education
Institution, degree, degree type, year graduated
Certifications
Courses, diplomas, certificates, security clearance and more
Skills
Detailed taxonomy of skills, leveraging a best-in-class database containing over 3,000 soft and hard skills
Summary
Candidate summary and objective
Language(s)
Language(s) spoken
Referees, publications, etc.
Don’t see a field that is of particular interest? Get in touch and we’ll add it to our roadmap.
Make smarter hiring decisions
with clearer candidate data.
Whether you’re a hiring manager, a recruiter, or an ATS or CRM provider, our deep-learning powered software can measurably improve hiring outcomes.
Frequently asked questions:
In a nutshell, it is a technology used to extract information from a resume or a CV.
Modern resume parsers leverage multiple AI neural networks and data science techniques to extract structured data. Typical fields being extracted relate to a candidate’s personal details, work experience, education, skills and more, to automatically create a detailed candidate profile. The extracted data can be used for a range of applications from simply populating a candidate in a CRM, to candidate screening, to full database search. These tools can be integrated into a software or platform, to provide near real time automation.
1.Automatically completing candidate profiles
Automatically populate candidate profiles, without needing to manually enter information
2.Candidate screening
Filter and screen candidates, based on the fields extracted. Sort candidates by years experience, skills, work history, highest level of education, and more. Extracted data can be used to create your very own job matching engine.
3.Database creation and search
Get more from your database. It is not uncommon for an organisation to have thousands, if not millions, of resumes in their database. Resume parsing can be used to create a structured candidate information, to transform your resume database into an easily searchable and high-value asset
What benefits can high-quality resume parsing technology deliver?
Efficiency benefits, improved outcomes, better applicant experience, and more. See what benefits are relevant to you, below.
- Applicant Tracking Systems and HR technology providers can integrate a resume parser into their application, to save your end users time, and improve your offering
- Competing solutions are typically either integrated into your core offering, or sold as an optional add on via your marketplace.
- Job boards can streamline the candidate application process, but automatically extracting candidate information from resumes, in near real time.
- Pre-populated profiles can then simply be checked by an applicant, reducing the time they spend applying for a job, and improving your conversion
- In addition, an existing database of resumes can be mined, to create a database of structured data. This itself is a high-value asset to any job board, as it provides meaningful group-level insights about candidates
- Recruitment firms can receive hundreds of applications, for each job posting. This tool can be used to save time and place more candidates
- Manually creating profiles for each applicant is time consuming and tedious. Affinda can be used to automatically create candidate profiles.
- Reviewing each resume can be time consuming, but by leveraging our solution, a recruiter can rapidly screen candidates based on the characteristics that are most important for the role, e.g. years experience, industries worked in, highest level of education, and more.
- In addition, a database created by an automated AI solution outperforms when applying a traditional semantic recruitment technology such as semantic search.
- Whether you’re building a database, or looking to streamline a process, integrated parsing can provide high-value functionality.
- Investing in building a custom solution requires a significant time investment, an existing database of resumes, and expertise in artificial intelligence and data science
- Hiring teams must evaluate large numbers of applications for every job they post. A resume parsing solution, on the other hand, can batch-process these applications, freeing up time for more important tasks.
- It takes hours to hand-code a unique profile for every candidate. By using automated deep-learning analysis, an organization can create candidate profiles by the hundreds, in a very short time.
- The resume screening process wastes untold hours. With an automated approach, a hiring manager can screen applicants for specific skills and experience levels that are most relevant to each job role.
- If you’re applying for jobs, or planning to, our solution can help you to analyze and enhance your own resume.
- A full 95 percent of Fortune 500 companies use applicant tracking systems to pull data from resumes. Affinda’s solution shows you what’s being pulled — and what’s getting missed.
Good intelligent document processing requires a combination of technologies and approaches.
Our solution uses deep transfer learning in combination with recent open source language models, to segment, section, identify, and extract relevant fields:
- We use image-based object detection and proprietary algorithms developed over several years to segment and understand the document, to identify correct reading order, and ideal segmentation.
- The structural information is then embedded in downstream sequence taggers which perform Named Entity Recognition (NER) to extract key fields.
- Each document section is handled by a separate neural network.
- Post-processing of fields to clean up location data, phone numbers and more.
- Comprehensive skills matching using semantic matching and other data science techniques
- To ensure optimal performance, all our models are trained on our database of thousands of English language resumes.
You can upload PDF, .doc and .docx files to our online tool and Resume Parser API.
Affinda has the capability to process scanned resumes. We use best-in-class intelligent OCR to convert scanned resumes into digital content. This is not currently available through our free resume parser. Please get in touch if you need a professional solution that includes OCR.
All uploaded information is stored in a secure location and encrypted. You can read all the details here.
Excel (.xls), JSON, and XML.
JSON & XML are best if you are looking to integrate it into your own tracking system.
Excel (.xls) output is perfect if you’re looking for a concise list of applicants and their details to store and come back to later for analysis or future recruitment.
We can build you your own parsing tool with custom fields, specific to your industry or the role you’re sourcing. Simply get in touch here!
Biases can influence interest in candidates based on gender, age, education, appearance, or nationality. Blind hiring involves removing candidate details that may be subject to bias. This allows you to objectively focus on the important stuff—like skills, experience, related projects. Affinda has the ability to customise output to remove bias, and even amend the resumes themselves, for a bias-free screening process. We use this process internally and it has led us to the fantastic and diverse team we have today! Please get in touch if this is of interest.
We’ve limited our online tool to 25 resumes at a time, to ensure everyone using it experiences super high-speed data extraction. Interested in a solution for automated uploads with an unlimited volume limit? Simply get in touch with one of our AI experts today.
Our Online App and CV Parser API will process documents in a matter of seconds. To keep you from waiting around for larger uploads, we email you your output when it’s ready. Don’t worry though, most of the time output is delivered to you within 10 minutes. If you’re looking for a faster, integrated solution, simply get in touch with one of our AI experts.
About Affinda.
We’re a passionate crew of developers, data scientists and AI engineers, on a mission to bring machine learning to the masses. We believe intelligent document processing solutions should be easy, affordable, and accessible to businesses of all sizes — so that’s exactly what we build every day.
Automate the back office
of any company.
This HR technology AI model is just one of the many applications of Affinda. Our technology can be leveraged across a range of industries and applications, to automate time consuming manual processes.
