Textkernel: Crafting a Labour Market without Information Boundaries
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Gerard Mulder, CEO
From predicting talent shortages to recruiting the right talent, AI can do it all if it’s done right. That however is the critical part. According to Gartner analyst, Nick Heudecker, over 85 percent of data science projects fail. Add to that the existing issues faced by the recruiters, the likelihood of getting a stable HR decision science system is already grim.
The lifecycle of skill is shorter than it used to be, making it difficult for the recruiters to capture and maintain an up-to-date view of the talent landscape. In case they manage to do so, sourcing the right talent for a role is another issue. It is further imperative to have a view of the future of the dynamic labour market across industries in order to be ahead of the competition in the recruitment landscape. For that, they need to have access to the mammoth of information hidden in the current and historical data. Considering these dynamics, Amsterdam-based Textkernel has developed a stable AI solution that employs a vast database and generates accurate recommendations for recruiters. “We help with making our clients' data analysable, searchable and by creating smart recommendations in their existing workflows,” says Gerard Mulder, CEO, Textkernel. “We give information about the job market in real time based on the current and the historic representation of all the jobs in Western Europe, US, and Canada.”
We help our customers with making the data searchable and creating smart recommendations in their existing workflows
Next to that, Textkernel delivers foundational technology for optimization, digitalization, and data science projects.It has been offering AI-enabled solutions to connect people and jobs from as early as 2001.Founded as a spin-off of three universities researching the application of machine learning to natural language processing, Textkernel uses machine learning and deep learning models to create taxonomies and ontologies to structure and enrich data. It classifies the customers’ data into taxonomies and ontologies to analyse and derive knowledge to understand the talent pool’s skills better. While an organization can use Textkernel’s AI tools to leverage talent already in their networks, close potential skill gaps, and anticipate workforce trends, a staffing agency can source and place top talent faster, engage with their database, and be the effective bridge between candidates and employers.
A major advantage of employing Textkernel is that the private data of an organization is connected with the publicly available data about the labour market to mimic what’s actually happening in the world, making the information provided truly actionable. That’s why one of the global leaders in staffing is now deploying Textkernel’s solutions across geographies. With multiple offices in various locations, the client organization was facing a challenge in having a quick look at the talent pool to source and address the job requisitions in an effective manner. Textkernel employed their taxonomies to structure the data based on the historic demand that the organization received over time. “We helped them see trends in that data and create particular profiles of people who had been sourced,” says Mulder. By predicting the demand of customers, the client was able to source ahead of the demand (just in time) the right amount of talent that they could place across organizations, and therefore, their placement ratios increased. In addition, Textkernel’s technology enabled them to compare every incoming candidate with all their jobs, make suggestions to them, and increase the ROI on their Recruitment spend.
It was made possible because Textkernel’s technology has been able to gain a holistic view of the clients’ data as well as the entire recruitment landscape, taking the information boundaries out of the equation. Mulder believes that a labour market, without information boundaries, creates a more prosperous world for people and companies, and that’s what the team at Textkernel is aiming for.