Taxonomy Unification AI for Skills Normalization

A NASDAQ-listed staffing and professional-services firm engaged Taller to build an AI taxonomy engine to normalize skills and job-description language across its recruiting data.

The client’s recruiting data was spread across many sources: job descriptions, candidate profiles, resumes, and internal recruiting workflows. But the language describing it was inconsistent. The same skill could be described several different ways, and the same role title could mean different things depending on the employer, industry, seniority, or business context. The titles "developer," "software engineer," "full-stack engineer," and "application engineer" might overlap heavily in one setting yet describe very different profiles in another.

That inconsistency created a structural problem for AI matching: even strong matching models produced weak results when the underlying language was fragmented. If skills were not normalized, if role families were not represented consistently, and if job descriptions were not converted into a common taxonomy, the system could not reliably compare one job to another, or one candidate to one job. The client needed an AI-driven taxonomy layer that could understand recruiting language, cluster skills and roles, and produce a consistent picture of demand across the business.

Taller designed and built an AI taxonomy engine for recruiting data, combining natural-language processing (NLP), unsupervised learning, supervised classification, deep learning, and custom data-engineering pipelines. The goal was a proprietary taxonomy that could represent jobs, skills, and role families consistently across the client’s data.

The first layer handled skill detection. Taller built NLP pipelines to extract skills from job descriptions and candidate profiles, catching both explicitly named skills and related terms expressed in different wording, so the system could tell that two differently worded descriptions were asking for the same underlying capability.

The second layer handled grouping and normalization. Taller used unsupervised clustering and nearest-neighbor similarity analysis (which group items by how alike they are without being told the categories in advance) to identify natural clusters in the data: skills that commonly appeared together, descriptions belonging to the same functional area, and role families that were similar in meaning even when their titles were not.

The third layer built the taxonomy itself. Taller used the models' output to construct a normalized skills-and-jobs taxonomy tailored to the client’s recruiting business, learning from the client’s own market data and recruiting patterns rather than relying only on generic public taxonomies. Job descriptions were then transformed into structured representations using this taxonomy, so they could be compared, searched, classified, and matched far more accurately.

The fourth layer turned the taxonomy into a service. Once jobs and skills were normalized, the system could classify roles by business area, detect demand patterns, improve job-order matching, and support candidate scoring. Other recruiting workflows could draw on it directly, turning fragmented job language into a consistent, machine-readable foundation for downstream AI.