We work at the intersection of ML Research and ML Engineering. To a great extent, it is hard to decouple these two disciplines. ML, at its heart, is an empirical discipline. The theoretical foundations of ML are sound in certain areas and evolving in other areas as we continue to push the envelope.
The projects we work on involve natural language processing, computational linguistics, large language models (LLMs), networking, security, knowledge acquisition, insight generation, and related areas.
We firmly believe in the “Farm to Table” approach to ML: where possible, we curate our own data to train our own models, and productize them through our unique ML operations (MLOps) approach for better customer experience. As part of this process, we gather new insights which we publish at peer-reviewed conferences and disseminate as technical talks.