The information retrieval from relational database requires professionals who has an understanding of structural query language such as SQL. TEXT2SQL models apply natural language inference to enable user interacting the database via natural language utterance. Current TEXT2SQL models normally focus on generating complex SQL query in a precise and complete fashion while certain features of real-world application in the production environment is not fully addressed. This paper is aimed to develop a service-oriented Text-to-SQL parser that translates natural language utterance to structural and executable SQL query. We introduce a algorithmic framework named Semantic-Enriched SQL generator (SE-SQL) that enables flexibly access database than rigid API in the application while keeping the performance quality for the most commonly used cases. The qualitative result shows that the proposed model achieves 88.3% execution accuracy on WikiSQL task, outperforming baseline by 13% error reduction. Moreover, the framework considers several service-oriented needs including low-complexity inference, out-of-table rejection, and text normalization.