Python's dynamic type system, while offering significant flexibility and expressiveness, poses substantial challenges for static analysis and automated tooling, particularly in unannotated or partially annotated codebases. Existing type inference approaches often depend on existing type annotations or on deep learning models that require extensive training corpora and considerable computational resources, resulting in limited scalability and reduced interpretability. We introduce Typify, a lightweight, usage-driven static analysis engine designed to infer precise and contextually relevant type information without relying on statistical learning or large datasets. Typify integrates symbolic execution with iterative fixpoint analysis and a context-matching retrieval system to propagate and predict type information across entire projects. By constructing and traversing dependency graphs in an execution-aware manner, Typify accurately connects function calls to their definitions and infers usage-based type semantics, even in complex, interdependent modules. We evaluate Typify on a diverse corpus of real-world Python repositories, including the ManyTypes4Py and Typilus datasets, benchmarking its effectiveness in predicting types of variables, arguments, and return statements. Results from the evaluation show that Typify consistently matches or surpasses state-of-the-art deep learning-based systems such as Type4Py and HiTyper, as well as industry-standard static type inference tools like Pyre. Our findings demonstrate that usage-driven, retrieval-based inference can match or exceed the accuracy of data-driven methods, offering a practical, interpretable, and computationally efficient alternative for large and evolving Python codebases.