Enterprise Grade Resume Parser & API

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

Affinda serves a wide variety of teams: Applicant Tracking Systems (ATS), Internal Recruitment Teams, HR Technology Platforms, Niche Staffing Services, and Job Boards – ranging from tiny startups all the way through to large Enterprises and Government Agencies.

Affinda can process résumés in eleven languages— English, Spanish, Italian, French, German, Portuguese, Russian, Turkish, Polish, Indonesian, and Hindi.

Good intelligent document processing – be it invoices or résumés – 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.

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.