Taxonomy Unification AI

The client is a NASDAQ-listed professional-services firm and one of the longest-running partnerships in the portfolio — a multi-year relationship that started with internal engineering services and expanded into a delivery channel serving the partner's external clients across consumer goods, industrial manufacturing, and professional-services markets. The partnership has placed thirty-plus engineers inside the staffing firm's own teams and more than 180 across the partner's external client work, with thirteen distinct projects.

Inconsistent data taxonomies across the recruiting platforms the firm operates were running into the staffing firm's AI ambitions repeatedly. Even the best matching algorithm produces low-confidence outputs when the source data is wrong in the slow ways that compound across systems; same role title meaning different things across employer contexts, same skill described with different vocabulary at different career levels.

Taller deployed the integrated ML, deep-learning, and NLP solution that unifies and optimizes the taxonomies, centralizes the data, and enables accurate job-order matching and candidate probability scoring at production scale. The Python and SQL pipeline handles the data extraction and normalization rules that catch the easy variants; the deep-learning model handles the harder cases. FastAPI exposes the unified taxonomy as a service the rest of the recruiting stack consumes; Selenium drives the data harvesting against the platforms that do not expose APIs; Docker keeps the deployment surface consistent across Azure DevOps's CI/CD pipeline; Azure Data Lake and Blob Storage hold the unified data substrate.

Taller's solution produced a fifty-percent reduction in data discrepancies, a thirty-percent improvement in candidate-matching accuracy, and a forty-percent increase in recruiting efficiency.

Taxonomy quality is the structural precondition for AI matching to work in recruiting; without it, every model investment compounds against the underlying data problem rather than against the commercial outcome. The unification engagement is the operational decision that converts the rest of the firm's AI investments into capability rather than into research.