Trajectory data analysis, e.g., trajectory summarization, imputation, prediction, and classification, has been fundamental to widely used applications. Even though several research efforts have been dedicated to develop numerous algorithms for trajectory analysis, there is an apparent lack of full-fledged systems that support a myriad of trajectory analysis tasks. The main reason is that each introduced algorithm employs new methods and data structures that are tailored to one specific trajectory analysis task. This paper presents KAFY; a full-fledged system that supports a myriad of trajectory data analysis tasks. KAFY leverages the recent advances in Natural Language Processing (NLP) where the transformer architecture is introduced as a system infrastructure to build large language models that can be fine tuned to support various NLP tasks. The main idea of KAFY is that instead of training a transformer architecture with a (spoken) language to produce (language) models, it trains it with the (unspoken) trajectory language to produce (trajectory) models. KAFY is an extensible system where its users can extend it with more transformers and/or trajectory operations. The first release of KAFY employs three transformers and supports five trajectory operations. Experimental results from a real deployment of KAFY show that it either outperforms or gives similar performance to existing baselines in all its supported trajectory operations.