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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.
The Engineering Pendulum: My journey between code and leadership
Careers in tech don't move in straight lines. Eniko shares her journey swinging between engineering and leadership, and how TechWolf's dual-track culture lets engineers explore both paths without having to choose.
Your foolproof guide to failing at skills
Want to guarantee your skills-based transformation is a multi-million dollar disaster? This guide uses Charlie Munger’s "inversion" method to walk you through the most efficient ways to fail—from keeping skills trapped in an HR silo to buying expensive tech without a strategy. By identifying these common "stupid" mistakes, you can finally clear the path toward a talent strategy that actually drives business value.
From people analytics to workforce intelligence: Why banking needs a "data lake" for skills
In a regulated industry like banking, failing to identify internal skills is a risk management failure. Former ABN AMRO leaders Frank van den Brink and Patrick Coolen argue that banks must move beyond "social architecture" toward a "technical architecture" for their people. This transition from descriptive analytics to prescriptive workforce intelligence requires a "data lake" of skills to bridge the gap between today’s supply and tomorrow’s strategic demand. The article challenges CHROs to stop benchmarking peers and start building as "anticipatory architects" who prioritize business outcomes over HR for HR.
