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Avature

A Commitment to AI Excellence

Avature AI builds on over a decade of experience with multi-lingual text processing, semantics and machine learning. Since the release of our resume parser in 2013, we have continued strengthening our AI strategy, making this technology one of the building blocks of the Avature platform. As such, it powers an increasing number of features that have the potential to transform the experience of all users across the talent lifecycle.

Our continual investment in R&D ensures that the platform remains on the leading edge of intelligent enterprise software. At the same time, our knowledge and experience – forged through years of working with global organizations and government agencies – allow us to build algorithms that can make accurate predictions and optimize user experience for organizations looking to achieve breakout HR performance.

Avature’s Approach to AI Development

The conversation around artificial intelligence is loud. But cutting through the noise to seize the AI opportunity is a strategic imperative. Which is why we focus our development on delivering tangible value to HR based on the four principles below:

Organic development

We build algorithms in-house and train them on high-quality HR-specific data to better align with customers’ needs.

Cross-platform AI

The Avature platform’s architecture allows you to leverage the AI engine to enhance any use case where it adds value.

Configurable AI

We grant users full transparency and control of the system’s inner workings so it supports decision-making.

Unbiased AI

We promote bias-free processes by excluding personal traits or historical human decisions from the data we train our models on.

Organic development

We build algorithms in-house and train them on high-quality HR-specific data to better align with customers’ needs.

Cross-platform AI

The Avature platform’s architecture allows you to leverage the AI engine to enhance any use case where it adds value.

Configurable AI

We grant users full transparency and control of the system’s inner workings so it supports decision-making.

Unbiased AI

We promote bias-free processes by excluding personal traits or historical human decisions from the data we train our models on.

Discover Our AI-Powered Features

Our approach to AI development results in a robust set of features that enhance recruiting and talent management processes.

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The Brains Behind Avature AI

The Machine Learning (ML) team sits at the heart of our AI innovation and is in charge of continuously enhancing our models through cutting-edge research and development. But making AI a core element of the Avature platform is not a one-team job. The ML team’s work is harnessed by two others dedicated to bringing AI to the UI in the form of user-friendly features.

One of them is the Natural Language Processing (NLP) team, which is responsible for improving our matching engine, skills framework and resume parser, among other capabilities.

The second is the Chatbots team, focused on developing and maintaining our conversational AI chatbot and constructing a database to continue improving our language processing and generation capabilities.

Blazing the AI Trail: Our Team’s Research

We are not trend followers. We are trendsetters. The NLP team actively contributes to the AI community by publishing its findings on Avature AI in peer-reviewed journals and presenting its advancements in expert conferences.

2023

Normalization of Education Information in Digitalized Recruitment Processes

Extracting information from a resume is usually a two-stage process. Our ML team proposes using a neural network architecture to tackle both stages simultaneously. The model improves system efficiency and resume parsing accuracy in 7 languages.

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2023

Resume Parsing as Hierarchical Sequence Labeling: An Empirical Study

In this paper, our team normalizes education information extracted from resumes by transforming it into level/field of study. They also define a new taxonomy for fields of study and show how the process can be applied to candidate-job matching.

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2022

Learning Job Titles Similarity from Noisy Skill Labels

Other ML models measure the semantic similarity between job titles using supervised learning techniques trained on vast amounts of manually labeled data. We propose an unsupervised representation learning method that is as effective and less costly.

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Bridging the Commercial and Academic Landscapes

Recently, Avature was selected by the Ministry of Digital Transformation of Spain to launch the first "Chair in Artificial Intelligence and Language Technology” in partnership with the University of the Basque Country (UPV/EHU)’s world-class HiTZ Center for Language Technology.

In the fast-evolving landscape of AI, this chair brings the academic and commercial perspectives together to foster research, safeguard the development of AI and maximize its impact.

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The Safe Choice

Avature’s approach to AI is guided by the Artificial Intelligence Risk Management Framework (AI RMF 1.0) as outlined in NIST AI 100-1. We prioritize staying up to date with the latest regulations, as evidenced by the Automated Employment Decision Tools audit we recently conducted in compliance with the NYC Bias Audit Law.

We design our AI to be a decision-support system where the outputs are always explainable, accessible and adjustable. This makes Avature the ideal choice for enterprises looking to deploy safe AI to optimize recruiting, talent management, employee training and re-skilling at scale.

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Additional Resources

Create Digital Experiences With AI-Enabled Recruiting Automation

Learn the use cases where AI-enabled recruiting automation can deliver the most value, the attributes to look for in a successful platform and how to set the right expectations for your technology partner.

Making Sense of Skills: Neural Network Models for Skills Semantics

Avature’s AI expert, Rabih Zbib, gives us an in-depth look on how skills semantics and embeddings work with the help of AI and machine learning.

A Deep Dive Into Avature’s Job Titles Similarity Model

Our Machine Learning research team has developed an innovative approach to determine the semantic similarity between job titles, and we’ve gathered the most noteworthy points of that work in this blog.