
Chengiz (; subtitled as The One Who Has No Boundaries No Limits) is a 2023 Indian Bengali-language epic period gangster action thriller film co-written and directed by Rajesh Ganguly. Based on a story written by Neeraj Pandey about Calcutta underworld, the film was produced by Jeet, Gopal Madnani, and Amit Jumrani under the banners of Grassroot Entertainment and Jeetz Filmworks. It stars Jeet in the titular role, as well as in dual role, alongside Susmita Chatterjee, Rohit Roy and Shataf Figar in another pivotal roles. The film shows the situation of Kolkata from mid 70s to late 90s following
The movie revolves around Chengiz, an underworld mafia from the 1970s to the mid-90s that ruled the streets of Calcutta.
Cast
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via Wikidata · CC0
Chengiz (; subtitled as The One Who Has No Boundaries No Limits) is a 2023 Indian Bengali-language epic period gangster action thriller film co-written and directed by Rajesh Ganguly. Based on a story written by Neeraj Pandey about Calcutta underworld, the film was produced by Jeet, Gopal Madnani, and Amit Jumrani under the banners of Grassroot Entertainment and Jeetz Filmworks. It stars Jeet in the titular role, as well as in dual role, alongside Susmita Chatterjee, Rohit Roy and Shataf Figar in another pivotal roles. The film shows the situation of Kolkata from mid 70s to late 90s following the journey of a child named Jaidev Singh who struggles with his life since his childhood and later becomes the dreaded drug lord named Chengiz in the Calcutta underworld.
The film was announced in March 2022, being the 55th film of Jeet as the leading actor. Principal photography commenced in April 2022 in Kolkata, with additional shoots in Morocco and Thailand, and concluded in November 2022. The film's music was composed by Aneek Dhar and Kaushik-Guddu, with cinematography and editing handled by Manas Ganguly and Malay Laha respectively. The action sequences of the film were designed by Stunt Silva, while the dance sequences were choreographed by Imran Sardhariya.
Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).