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TechWolf is on a mission to redefine how organizations understand and leverage skills data.
We are dedicated to developing specialized AI that is built exclusively to solve the skills intelligence problem—delivering unparalleled accuracy, fairness, and transparency.
Our AI vision
We envision a world where AI-powered skill data drives workforce transformation, enabling businesses to make informed talent decisions, foster internal mobility, and future-proof their organizations.
Our committment to the AI Act & responsible AI
As part of our commitment to responsible AI, TechWolf aligns with the ISO 42001 standard for AI management and is a proud member of the AI Pact, ensuring we adhere to the highest industry standards for ethical AI development.
Responsible, Transparent, Fair
TechWolf’s AI follows a strict charter that ensures responsibility at every step.
Bias-resistant & fair AI
AI models go through audits to prevent discrimination
Explainable AI
Clear and transparent decisions, not black-box algorithms.
Data security
Protection built into every AI model.
Open innovation
Research and open-source contributions over restrictive patents. ( publishing over patenting)
2.1B+Training Data Points
50+Most-cited AI in workforce intelligence
10K+Research Hours
7Papers published across top AI journals
15Models running in production
5000+Open source downloads
SkillMatch
We construct and release SkillMatch, a benchmark for the task of skill relatedness, based on expert knowledge mining from millions of job ads.
On the Biased Assessment of Expert Finding Systems
This study provides an analysis of how these recommendations can impact the evaluation of expert finding systems.
CareerBERT
Career Path Prediction using Resume Representation Learning and Skill-based Matching
Extreme Multi-Label Skill Extraction Training using Large Language Models
TechWolf leverages Large Language Models (LLMs) to accurately detect both explicit and implicit skills, linking them to a comprehensive skill ontology. Our innovative approach generates high-quality synthetic training data, combined with contrastive learning, leading to a 15-25% improvement in skill extraction accuracy compared to traditional methods.
Ranking the skills required for a Job-Title
In this paper, we describe our method for ranking the skills required for a given job title.
Skill extraction
benchmark for job ads
JobBERT 2
This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2 on the generator dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Innovating together with
leading universities & institutions
Collaboration powers our progress. By partnering with top universities and research institutions, we tackle the toughest challenges in AI and HR tech. These partnerships ensure we stay ahead in skills data innovation, blending academic insight with real-world application.
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Build AI that shapes the future of work
Join a team of innovators driving AI-powered workforce intelligence. Explore opportunities to make an impact.
Blog
Relevant sources
From guides to whitepapers, we’ve got everything you need to for your skills journey.
Steps to building a skills-based hiring strategy
Traditional hiring methods—relying on resumes, experience, and outdated job descriptions—are no longer effective. With 85 million jobs projected to be unfilled by 2030, companies need a skills-first hiring approach to stay competitive. However, 92% of organizations lack reliable workforce skills data, making it difficult to identify skill gaps, match talent to roles, and make informed hiring decisions. A skills-based hiring strategy starts with accurate, AI-driven workforce data to map job roles, define skill-based job descriptions, and optimize hiring decisions. Companies that adopt this approach can reduce hiring costs, improve internal mobility, and ensure fair, skills-based compensation.
Why traditional skill gap analysis is broken—And how AI fixes it
Traditional skill gap analysis is outdated, unreliable, and too slow to keep up with today’s evolving workforce. It relies on self-reported assessments, manager bias, and static surveys—leading to inaccurate data, misaligned reskilling efforts, and costly external hiring. AI-driven skill intelligence eliminates bias, updates workforce skills in real time, and predicts future talent needs to ensure businesses stay competitive. By automating skill tracking and workforce planning, AI helps organizations proactively reskill employees, reduce hiring costs, and optimize talent mobility.
