ColBERT is a fast and accurate retrieval system built on BERT that encodes each passage into a matrix of token-level embeddings. At search time, it embeds queries into similar matrices and employs scalable vector-similarity MaxSim operators to efficiently find contextually matching passages. This late-interaction approach enables it to surpass single-vector representation models in quality while maintaining the ability to scale to large document collections.