Zeva is Zensar’s voice-based enterprise personal assistant. With features of enterprise integration, email integration, and Google search integration. Zeva has an in-built self-learning capability based on its usage pattern. Built on an AI-based Q&A system, Zeva maintains context in the conversation using voice authentication and offline speech features.
Feedback based continuous learning: designed and implemented a feedback loop for improving the answers by ZEVA. Feedback is provided through a like/dislike button. Trained an ML model to continuously learn from feedback.
Unstructured text retrieval: implemented a custom fine-tuned model based on BERT to retrieve answers from unstructured text. Improved accuracy by approximately 48% compared to the previous IR model.
Refactoring monolith to microservices: collaborated with the team to convert the previous monolith version of ZEVA to the current microservices architecture.
