In the era of AI-driven transformation, contact center leaders face a critical challenge: balancing the demand for high-precision natural language processing tools with the need for resource-efficient, scalable solutions. This study presents industry case of integrating TRIZ to achieve this goal, enabling the development of speech analytics products tailored to high-load contact center environments. The paper describes how TRIZ helps eliminating the existing contradiction between the precision of language models and their computational resource intensity using: root-conflict analysis, contradiction identification and solving, inventive principles and other tools and methods. As an industrial case study, the authors define the task of named entity recognition for searching for names and addresses in a Russian-language text and matching them with relational database records for client identification and authorization in corporate services when interacting with digital assistants. A root conflict analysis of the task is performed, and TRIZ tools are used to find methods for searching and classifying name and address identification tokens. By combining TRIZ tools and features of large language and machine learning models, the team implements hybrid service for client identification and data retrieval with proven efficiency. The approach used for the development accelerated product development timelines, validating the synergy between traditional R&D approaches, TRIZ and AI-driven innovation.