This system was built by adapting multiple bleeding edge research papers into a full production AI system. The system accepts streams of realtime flight data, and thus has extremely low latency requirements. It then performs unsupervised clustering of timeseries data in order to flag potentially dangerous flight paths.
Our location recommender utilized natural language embeddings of massive databases of location details to match locations to groups of users and their respective social events. This engine allowed for curated social events to be built automatically with everyone’s preferences in mind.
Computer vision model that takes in an image of a receipt and outputs an itemization of all charges, each tagged with relevant item categories. To make this work properly, we needed to create a custom pipeline comprised of many deep neural networks. The first model performed object recognition, passed the output to an OCR model, then a model that we trained to understand receipt geometry, and a finally an NLP model that performed classification on the types of charges.