AI has always been our method of choice in our mission to accelerate staffing, recruitment and HR processes. In fact, we have pioneered AI solutions for the recruitment domain over 20 years ago, and have been monitoring and applying developments in AI ever since. And that’s not just because it’s an exciting technology: AI actually makes our customers more effective! Whether it’s about automating data entry from CVs or vacancies, shortlisting candidates for jobs, or enabling market analytics, AI-driven software can hugely improve process efficiency. And over time we’ve learned that embracing new developments in AI is key to making sure that the quality of these systems gets ever better. 

With all the media frenzy these days, many people would be surprised to learn that AI has been around since the invention of computers. What has changed over the years is the AI algorithms used to make computers intelligent.

The early days

The very AI algorithms of the 1980s consisted of a set of hard-coded assumptions and rules made by domain experts. Think of rules like “if a CV contains a 10-digit number, then it must be a phone number”, or “whatever follows the phrase “Name:” is someone’s name”. It turns out that language is way too complex to be captured with rules (phone numbers can be written with dashes in between digits, the phrase “Name:” can occur in phrases like “School Name:”). Rule-based AI systems tend to grow into a large stack of exceptions on top of exceptions: error-prone and difficult to maintain. Practical applications of such systems were out of reach.

Statistical machine learning

In the late 1990s statistical machine learning came to the rescue. Instead of writing rules manually, statistical algorithms (e.g. Hidden Markov Models in the early 2000s) can infer rules and patterns from annotated data. Those rules are generally better than those found by human engineers: they strike the right balance between being specific and generalizable, and use patterns in the data that humans wouldn’t have seen. Employing machine learning models in combination with various rich data sources, Textkernel achieved best-in-breed accuracy levels on the problems it set out to solve.

Introducing Deep Learning

But early machine learning models still had their limits: they were not able to digest a lot of context and still relied heavily on human expertise (of which signals/features are relevant for specific problems). To understand what a given word means, they would basically only consider the words in their direct neighborhood. A good understanding of a CV or job ad, however, requires understanding the context of the entire paragraph or even the full document.

This is why we invested in upgrading our models to a special kind of machine learning technology: Deep Learning. These somewhat more complex neural networks allowed for a much more contextualized form of document understanding. In addition, they could figure out by themselves which textual features are relevant to solve a given task. Deep Learning took academia by storm in the 2010s and in 2017 it was mature enough to be applied to business problems. Once we applied to parsing, it led to another substantial boost to our accuracy levels.

Recently we’ve been closely monitoring one of the most disruptive developments in language technology so far: Large Language Models (the technology behind ChatGPT) and their impressive ability to perform well on just about any language task and to encode knowledge of the world.

What are LLMs and why do they work so well?

Language models are AI systems with a surprisingly simple objective: “simulate” language. Given a sequence of words, their task is to predict the next most likely word. For example, “bank” or “ATM” are the most likely words that would follow the sequence “I withdrew some money from the …”. Language models have been around for about 30 years. In the past few years, people have been building language models using increasingly bigger neural networks with a special attention mechanism (transformers) and using more and more language data (see table below). It turns out that these Large Language Models (LLMs) start exhibiting abilities that even surprised their creators:

• Performing language tasks: in order to “simulate” language, they become very good at language tasks. They can generate high quality text, summarize text, rewrite text in specific styles, etc.
• Encoding knowledge of the world: language can not be simulated well without world knowledge (e.g. you can not write good quality text about Obama unless you know he was a president of the USA). LLMs magically capture and represent that knowledge just by reading lots of text. 
• Some cognitive skills: LLMs try to simulate text that was manually created by people by applying various cognitive skills: inference, deduction, simple reasoning, etc. LLMs seem to develop – or at least mimic – such skills in order to be good at simulating text. It is hypothesized that the size of the neural network and attention mechanism is key for this. In addition, since their training data also includes computer programs, their documentation and the text around them, LLMs are surprisingly good at generating code too. In fact, LLMs can even learn new skills.

Key ingredients in LLMs

It turns out that for LLMs the saying is true: “If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck”. In other words, in the process of simulating human language, these systems have become very good at mimicking the very skills and knowledge that produced that language.

LLMs in recruitment: potential and limitations

The HR media are flooded with suggestions on how ChatGPT and similar tools can be applied to streamline workflows. Ideas range from automated content generation (vacancies, interview questions, marketing content) to improved candidate screening and automated communication. Some of these will be more fruitful than others, but one thing is for sure: recruitment and HR are among the many industries that will be shaken up, if not revolutionized, by this new generation of AI technology.

Apart from giving rise to innovative products, it’s also clear that LLMs will help existing AI-based tools reach higher accuracy and improve their user experience. That’s also true for our software: just like we’ve seen that previous AI developments brought significant quality improvements, LLMs will most certainly benefit the quality of our software for document understanding, candidate sourcing and matching, data enrichment, and analytics. In the next parts of this blog series we will share how we’re using the technology at the moment, and what’s to come. 

Not so fast?

Having pursued AI-driven innovation for over two decades, at Textkernel we are well aware that technological breakthroughs are not merely reasons for excitement. And we’re not the first to note that the use of technologies like ChatGPT come with risks and limitations. There are technical limitations concerning scalability and cost. For example, building LLMs is a very complex and very expensive process. It is estimated that it cost OpenAI 4 million dollars to train their GPT-3. Keep in mind that ChatGPT is based on an even newer version, GPT-3.5. At least for the near future, it is envisioned that companies will use LLMs from a small number of providers rather than build an in-house LLM. Running LLMs is also costly which in turn affects the cost of services built on top of them.

Lastly, and not to be underestimated, there are valid concerns about data privacy, transparency and bias. These concerns should be taken very seriously, and the various upcoming forms of AI legislation, such as the EU AI Act and the NY AEDT Law, will help ensure these concerns are treated seriously.

Stay tuned to the next parts of this blog series to hear more about how LLMs relate to AI legislation and how we envision combining compliance with cutting-edge innovation.

You can’t have missed it: Artificial Intelligence tools like ChatGPT are taking the world by storm. They are making waves in HR and recruitment media with suggestions on how they can streamline workflows. From generating content (such as job vacancies, interview questions, and marketing materials) to AI-aided candidate screening and communication, ideas are plentiful. While some of these will be more fruitful than others, it is certain that recruitment and HR are among the many industries that will be impacted, if not revolutionized, by this new generation of AI technology.

In this blog, we explain what this technology is about and how it relates to previous generations of AI. In subsequent blogs, we will delve deeper into the limitations you should be aware of and evaluate the pros and cons of applications in recruitment technology.

A brief history of AI disruptions

The adoption of AI algorithms in recruitment and HR processes has accelerated over the past few decades. Early rule-based AI systems of the 80s had limitations due to their error-prone and difficult-to-maintain nature, which affected their quality.

Machine learning

The introduction of statistical machine learning technology started to change this, as it could help automate language tasks with substantial accuracy (e.g. data entry from CVs). However, these systems were still limited in their ability to digest context and required more human engineering than desired.

Deep learning

These drawbacks were overcome by a special kind of machine learning technology called deep learning (see this blog and this one), which excels at learning complex patterns without the need to tell it what to learn. It turned out that these capacities could be further amplified by making deep learning models larger and feeding them with more data. Scaling the model and data size eventually gave rise to a family of deep learning models called Large Language Models (LLMs). These include OpenAI’s GPT-4 and its chat-oriented cousin ChatGPT, as well as Google’s LaMDA and other industry competitors.

*Want to know more about Textkernel’s journey through these different stages, and the impact each one of them had on recruitment technology? Come back to our website to see our upcoming blog.

LLMs

LLMs are among the most disruptive developments in language technology. LLMs are AI systems that simulate language and try to predict the most likely word to follow a sequence of previous words. By using increasingly larger neural networks and more language data, LLMs have started to exhibit abilities that surprised even their creators, such as generating high-quality text, summarizing text, and rewriting text in specific styles. The possibilities of LLMs are exciting, but with great power comes responsibility.

Not so fast?

Having pursued AI-driven innovation for over two decades, at Textkernel we are well aware that technological breakthroughs are not merely reasons for excitement. And we’re not the first to note that the use of technologies like ChatGPT come with limitations and risks. There are technical limitations concerning scalability and cost. But more importantly, valid concerns exist about data privacy, transparency and bias. These concerns should be taken very seriously, and upcoming AI legislation, such as the EU AI Act and the NY AEDT Law, will help ensure they are addressed.

Stay tuned for the next part of this blog series to hear more about how LLMs relate to AI legislation, how Textkernel envisions combining compliance with cutting-edge innovation and how we are already putting LLMs in practice in a responsible manner.

*** Read more in the long version ChatGPT and LLMs: the next chapter on Textkernel’s AI Journey

The value of recruitment automation starts with quality data and as the leading AI-powered recruitment technology provider our AI is the foundation of successful recruitment automation.

We’re releasing a new Jobfeed user interface designed to take your lead generation process to the next level.

Business development and lead generation are vital components of the recruitment industry, and in today’s fast-changing, highly competitive job market and increased need for personalized services, staying ahead requires access to the right tools, technologies and information. That’s why we’re excited to announce the upgrade to our Jobfeed Portal, a powerful tool that provides access to over 3 billion current and historical job postings, with a dedicated focus on lead generation.

The new and improved Jobfeed Portal is faster, more intuitive, and designed specifically to help you identify and win new clients.

Faster

With fewer clicks, automated processes, and quick access to essential features, you can streamline your workflow and accomplish more in less time.

Starting a search now takes just two clicks, and continuing from your last search takes just one click, allowing you to stay up-to-date with the latest market labor changes, trends and requirements with ease.

More intuitive

One of the key improvements in the Jobfeed Portal is its more intuitive search function. Following  “what” and “where” logic, you can quickly find what you need.

Clear company overviews and “click to action” contact details make it easy to contact prospects directly from within the portal screen. The interface is intuitive, with a logical information layout, making it easy to navigate.

Improved lead generation functionality

In today’s highly competitive job market, having an edge in identifying and winning new clients is crucial, and the new and improved Jobfeed Portal offers just that.

Its user-friendly interface, comprehensive labor market data, and efficient lead generation capabilities make it an essential resource for staffing agencies and recruiters alike.

To help you get the most out of the new interface, we’ve created a video that explains all of the new features and improvements. Click here to watch the video now and see how the new Jobfeed portal can take your lead generation process to the next level.

With the upgraded Jobfeed Portal, you can stay ahead of the competition, identify new business opportunities, and tailor your offerings to the needs of your clients.

Partner with Textkernel’s Jobfeed, the labor market intelligence tool for professionals

Partnering with Jobfeed gives you access to best-in-class labor market data for organizational decision-making. Plus, with our new interface innovations, your lead generation process will reach unprecedented levels.

To find out how Jobfeed can provide you with a competitive advantage in an intensely competitive market, book a personalized demo now.

As a member of the Textkernel team, I am excited to announce our recent acquisition of Joboti. The Amsterdam-based company is dedicated to providing innovative candidate engagement technology, and with this acquisition, Textkernel’s global buy-and-build strategy takes another significant step forward, enabling us to offer even more value to our clients.

Gerard Mulder, CEO of Textkernel

This acquisition brings together two organizations with a shared vision of providing seamless, automated, and scalable solutions for recruiters and sourcing professionals. The combination of Textkernel’s cutting-edge parsing and matching technology and Joboti’s innovative candidate engagement technology creates a powerful platform for recruiters to find and engage with the right candidates with minimal effort.

Currently, recruiters are limited by a mainly manual process of finding the right candidates and then reaching out to them through social media, email, phone, or instant message to confirm their availability and interest. However, with the combination of our technologies, recruiters can quickly find relevant candidates through AI-powered match technology and engage with those who have the right skills and have indicated their interest, availability, updated skills, and even completed a vetting question.

Moreover, the integration of Joboti’s technology into our solutions will allow recruiters to automate communication workflows in the recruitment process, including job alerts, GDPR checks, pre-screenings, interview scheduling, and feedback messages. This will streamline the process of engaging with candidates, freeing recruiters to focus on higher value tasks such as starting meaningful engagements with available candidates.

Our customers can expect to see even more innovative features and products in the coming months and years. The combination of Joboti’s technology and our AI-powered recruitment solutions will provide advanced tools for engaging with candidates, improving the recruitment process, and shortening the time to hire. With the ability to keep candidate records up to date and engage only with available and interested candidates, recruiters can ensure a positive candidate experience.

We remain committed to innovation and making an impact on the recruitment industry. The acquisition of Joboti strengthens our position as a leader in AI-powered recruitment solutions, and we are excited to welcome the Joboti team to Textkernel. Together, we look forward to creating even more innovative recruitment solutions that will revolutionize the industry and help our clients achieve their recruitment goals with even greater efficiency and ease.

For more information and an FAQ about the acquisition, visit our website.  

About Joboti

Joboti is an Amsterdam-based start-up that specializes in cutting-edge recruitment technology. Founded in 2016 by Luuk van Neerven and Stephan Kockelkoren, the company offers multi-channel communication solutions  (such as  Whatsapp and SMS) and candidate engagement workflow automation to provide a seamless recruitment experience.

With increasing competition for talent improving candidate redeployment rates is an important goal for staffing agencies that deal with flex employment. However, knowledge about candidate availability and job fit are often missing in their systems. With this year’s acquisition of Akyla, Textkernel will be able to close this knowledge gap. We explain why Textkernel acquired Akyla and what steps you can expect from the cooperation in the future. 

In June 2022, Textkernel acquired the software company Akyla, a Dutch company considered a true best-of-breed solutions provider offering mid-office platform solutions for automated redeployment, efficient management of flex workers, time management and document processing.

Staffing organizations are currently experiencing a tough candidate shortage in the labor market. This situation will become more critical in the coming years due to workforce demographic changes in most western societies. In this situation, recruitment is becoming more and more expensive and time intensive. Candidates are precious and staffing agencies will want to have a high redeployment rate instead of continuing a post-and-pray methodology followed by time intensive recruitment processes. However, information about candidate availability – crucial for a successful redeployment – is often missing in front office systems where recruitment takes place.

The right data for smart business decisions

Textkernel’s acquisition of Akyla will add to our suite of solutions enabling staffing agencies to turn data into knowledge for smarter business decisions. As a mid office software solution, Akyla’s solutions assist customers in administrative tasks like onboarding, hourly registration, time interpretation, digital signing or vendor management. The Akyla solution connects front and back office systems and has direct access to both candidates and employers via a mobile app. They know when an assignment is ending and a candidate will become available.

Based on this data, customers using Akyla and Textkernel will benefit from matching results that are more tailored to their processes and the availability of candidates. They will be able to offer new opportunities to candidates at the right time, increase retention and avoid repeating the cost and time intensive recruitment, interviewing, selection and onboarding processes.

Combining the best matching engine and the best mid office system will positively impact the candidate engagement as well as the candidate experience and eventually result in lower turnover, better fill rates, lower cost-per-hire, lower time-to-hire. And apart from the staffing companies, the candidates will also benefit from a better redeployment: less financial risk and uncertainty which will result in a better image for staffing companies.

The end of 2022 is fast approaching. Emerging trends in candidate expectations, competitive job advertising, and data-driven capabilities have created more challenges and opportunities for HR professionals than ever before. We understand that to stay abreast with developments in the market our customers need to make quick informed decisions, and that our products need to make talent and labor market data meaningful and actionable. This requires continuous product investments and enhancements.  We are excited to close off 2022 by sharing the highlights of Textkernel’s recent and upcoming product enhancements, helping you to better understand, connect and analyze people and jobs.

Understand – Capture and maintain an up-to-date view of the talent landscape

It all starts with…processing and understanding unstructured candidate information and documentation into structured data that can be used to filter, search and rank candidates information. Our Resume/ CV parser tool enables you to automate candidate data processing for a faster, more efficient and more accurate process. What’s new…?

• We now support a whopping 25 languages for data extraction at a high accuracy level. Our most recent language additions include Chinese and Hebrew
• We’ve made significant increases to parsing accuracy; the latest including resumes from Romania, Colombia, Brazil and Portugal. See our testing results compared to our competitors.
• Instead of extracting one big list of skills, you will now see extracted skills associated with the work history, enhancing the relevance of the skills listed.   
• All customers now benefit from 80% more skills extracted, up to 20% reduction of noise, automatic future updates and skill normalization due to the upgrade to our skills parsing framework version 2.

Data on people and jobs tends to be messy: skills and job titles can be worded in different ways, and information might be locked in different sources and/or formats. Textkernel’s Data Enrichment APIs help standardizeand enrich job and skill data when parsing a Resume/Job. What’s new…?

• We’ve added 9 more new languages to the skills taxonomy!
• Supporting you to be compliant with local regulations, the professions taxonomy has been upgraded to support O*NET 2019 and local taxonomies in Germany, the UK and The Netherlands.

Connect – Make data actionable to match people and jobs better

Our Search! & Match! tools save you hours in sourcing and ranking the right candidates to jobs and visa versa.  Begin leveraging the value of your internal database and/or run a single search against multiple external sources. With a single click, an automated search can be created, based on a profile matched against internal jobs or matched against direct employer jobs on the market through Jobfeed. What’s new…?:

• Finding similar candidates (candidate-to-candidate matching) has been refreshed and improved, offering you yet another way of efficiently connecting people and jobs better.
• Matching is even more accurate with a focus on specific aspects of the candidate. In our dynamic templates it is now possible to generate preferred queries. For instance, education is very important for recent graduates, while it is less important when looking for very experienced candidates.
• Bulk actions on a large amount of search or match results will soon be much easier to perform! 
• ‘Source’ is our new, stand alone and simplified product for searching across multiple external candidate databases
• With private data analytics, it’s easy to compare your private data against the market demand. Now you can load data from your Textkernel Search index with enriched and normalized data from our taxonomies into your data warehouse to analyze easier. 
• Coming up, web profiles can soon be imported with a single click using our browser extension.
• To save you even more time, we will soon launch a new Automation API which can   auto-match candidates and jobs without you even asking for it!

Analyze –  Expose data to strategize at an individual and organizational level

The only way to stay ahead of competition in tight markets is to respond faster. Jobfeed provides realtime market insights which enable you to identify growth opportunities, differentiate yourself from competition, and dramatically save time and effort. We power you with excellent market expertise, competitor intel, and visibility into rapidly changing trends.
What’s new…?

• We continuously increase our data accuracy, latest accuracy updates include salaries and locations.
• Jobfeed is now available for 11 countries with our latest addition, Switzerland.      
• We are not done with adding countries, coming up: Australia, New Zealand and Japan.
• Improved candidate experience with the Apply URL added to the data model.
• Better display and increased user friendliness with our markdown formatting 
• New and improved Jobfeed user interface will be ready soon!

We work with the best in the business

Over a hundred partners across the globe offer Textkernel’s state-of-the-art AI-powered technology, from local champions to big international players like Salesforce, Bullhorn, SAP, Cornerstone, and Oracle. And we’re constantly growing our ecosystem. We are proud to share some significant enhancements in our Salesforce Connector;

• Implemented support so you can easily source and import external candidates into Salesforce
• Widgets to display best matches inside Salesforce right alongside the candidate or vacancy
• You can now easily import Jobfeed leads into Salesforce

We sincerely hope you share our enthusiasm! Feeling motivated and inspired to create your talent acquisition strategy for the year ahead? Read through our 4 recommendations for nailing your 2023 plan in between cherished moments with your loved ones this holiday season.

If you need assistance along the way, be sure to reach out via email at info@textkernel.com for a helping hand. Cheers to your success in 2023 and beyond!

Swiss labor market data now available in Jobfeed

Textkernel’s labor market analytics tool Jobfeed is now available for the Swiss job market. Being a multilingual country with higher salaries than other markets and a record-low unemployment rate, Switzerland is a very special labor market to analyze. We have done a deep dive into our data to introduce you to the Swiss job market statistics.

Jobfeed crawls over 1.000 Swiss websites and provides historic data from 130.000 organizations. The tool shows a total number of 270.000 open vacancies in Switzerland with 1,4 open vacancies per jobseeker. This record level of staff shortage is starting to threaten the country’s prosperity according to Swiss media sources. The situation is likely to change for the worse as a huge number of employees is going to retire in the next few years, leaving Switzerland with a lack of 1.2 million workers by 2035.

The Swiss job market in a nutshell

Let’s go for a quick data dive and discover some characteristics of the Swiss labor market that Jobfeed delivers to you with just a few clicks in a personalized and automated way.

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Who is posting jobs in Switzerland?

Jobfeed enables you to differentiate Direct Employers from Staffing organizations which makes it the perfect tool for recruiters trying to generate new business opportunities online. In Switzerland, roughly 34% of the vacancies are published by Staffing Agencies. Compared to other European markets, this percentage for job ads posted by Staffing Agencies is relatively low (roughly 50/50 in Germany or France).

Which positions are most in demand?

Nurses are by far the most needed profession in Switzerland representing over 2% of the Swiss vacancies. If we add the Nursing assistants to this (ranking third) the percentage is even higher with 3.4%. This is a huge issue for the Swiss labor market and the shortage of caregiving professionals might even increase in the following years. Three IT professions (Software Engineers, System Engineers or SAP Consultants) are amongst the Top 10 most needed professions, too.

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Identifying skill trends in Switzerland

Jobfeed covers all the skills that are requested online and makes them analyzable in a user friendly way. With this, you can easily identify skill trends and roles that are in high demand and adapt promptly to market changes. Let’s take a look at the required soft skills for the much needed profession Nurse and compare it with the French and German labor market. It is interesting to see that job ads in Switzerland make less mention of team work or interpersonal skills, but rather highlight the management of stress and adaptability skills.

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Regarding professional skills, Swiss job ads tend to highlight lots of different key areas. Skills like “Multidisciplinary Approach”, “Long-Term Care”, “Nursing” and “Medical Records” are all at roughly 20%. Job descriptions in Germany and France seem to be more general with the very broad skill “Nursing” being by far the most important. 

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About Jobfeed

Jobfeed Switzerland provides realtime market insights which enable you to identify growth opportunities, differentiate from competition and dramatically save time and effort. We power you with excellent market expertise, competitor intel and visibility in rapidly changing trends. So in a few clicks you can:;

• Identify new customers for your staffing services
• React to skill trends and high demand roles
• Keep track of competition 
• Build stronger customer relations 
• Identify gaps in your market share

and much more… 

Check out the new Jobfeed for Switzerland now!