Company

Building a more transparent way to interact with enterprise data

Sentory was created to reduce the friction between business questions and database-backed answers while preserving the visibility and trust that enterprise teams expect.

Houcine Soudane

Founder, Sentory
MS Computer Science — DePaul University

Houcine Soudane is a database and enterprise systems specialist with more than 20 years of experience designing, operating, and optimizing large-scale data platforms. He holds a Certificate in Data Science and Mchine Learning: making data driven decisions from Massachusetts Institute of Technology (MIT) Schwarzman and Master’s degree in Computer Science from DePaul University in Chicago and has worked as a senior Oracle, MySQL, and PostgreSQL database and applications DBA and systems engineer across a wide range of public and private organizations.

Throughout his career, he has held senior full-time, consulting, and management roles supporting complex data environments for organizations including Stanford University, the University of Louisville, the New York City Housing Authority, LexisNexis Inc., General Electric, Barrick Gold Inc., Sonic Food Inc., Telephone and Data Systems Inc., and Flowserve Inc.

Through years of working with complex enterprise data systems, he saw the same pattern repeatedly: organizations collect enormous amounts of data, but answering straightforward business questions still often depends on technical bottlenecks. He founded Sentory to simplify enterprise analytics by enabling organizations to interact with their data using natural language and leveraging Artificial Intelligence capabilities while maintaining full SQL transparency and enterprise trust.

Database architecture Business Intelligence Enterprise systems Analytics infrastructure Oracle · MySQL · PostgreSQL Machine Learning & Artificial Intelligence
What Sentory believes

Core principles behind Sentory Platform

Transparency

AI-generated analytics should remain visible and explainable, especially when real decisions depend on the result.

Data ownership

Organizations should retain full control over their data environments and how analytics tools interact with them.

Simplicity

Asking useful questions about enterprise data should not require friction-heavy technical workflows.

Trust

Adoption grows when business users and technical teams can inspect, validate, and align around the same result.