Ben Quachtran is a Co-Founder and CTO based in New York with six years of hands-on experience building AI-driven backend systems and production conversational agents. He moved from engineering roles at Disney and Rasa—where he contributed backend improvements to widely used demo bots—to senior ML engineering positions before founding Pocket. Ben blends applied machine learning with pragmatic software engineering, shipping features like payment and transaction validation for financial chatbots and refactoring complex dialog actions to improve reliability. Educated at UCLA (BS/MS in Electrical and Computer Engineering), he combines academic rigor with startup grit and a track record of turning prototype ML assistants into maintainable production services. Notably, his open-source contributions to Rasa demos demonstrate an eye for robustness and real-world edge cases in conversational AI.
6 years of coding experience
9 years of employment as a software developer
Master of Science - MS, Electrical and Computer Engineering, Master of Science - MS, Electrical and Computer Engineering at University of California, Los Angeles
:tiger: Sara - the Rasa Demo Bot: An example of a contextual AI assistant built with the open source Rasa Stack
Role in this project:
Back-end Developer
Contributions:13 reviews, 75 commits, 28 PRs in 1 year 2 months
Contributions summary:Ben primarily focused on modifying and updating the actions code within the Rasa demo bot. Their contributions included refactoring existing forms, adding form validation, and adjusting the submission process. Several commits involve updating the action code to incorporate changes and bug fixes to integrate with features like response selectors and other enhancements. The user has also updated dependencies and requirements.
Contributions:11 reviews, 49 commits, 22 PRs in 10 months
Contributions summary:Ben primarily focused on enhancing the backend functionality of the financial services bot. They added new features for credit card payments and balance checks, including implementing insufficient funds validation. Moreover, the user made improvements to the transaction search functionality and bug fixes. These changes involved modifications to the core logic within the `actions/actions.py` file, indicating a focus on improving the bot's ability to handle financial transactions and user requests accurately.
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