diff --git a/src/quemb/shared/typing.py b/src/quemb/shared/typing.py
new file mode 100644
index 00000000..5f40d527
--- /dev/null
+++ b/src/quemb/shared/typing.py
@@ -0,0 +1,27 @@
+"""Enable barebone typechecking for the shape of numpy arrays
+
+Inspired by
+https://stackoverflow.com/questions/75495212/type-hinting-numpy-arrays-and-batches
+
+Note that most numpy functions return `ndarray[Any, Any]`
+i.e. the type is mostly useful to document intent to the developer.
+"""
+
+from typing import Tuple, TypeVar
+
+import numpy as np
+
+# We want the dtype to behave covariant, i.e. if a
+#  Vector[float] is allowed, then the more specific
+#  Vector[float64] should also be allowed.
+# Also see here:
+# https://stackoverflow.com/questions/61568462/what-does-typevara-b-covariant-true-mean
+T_dtype_co = TypeVar("T_dtype_co", bound=np.generic, covariant=True)
+
+Vector = np.ndarray[Tuple[int], np.dtype[T_dtype_co]]
+Matrix = np.ndarray[Tuple[int, int], np.dtype[T_dtype_co]]
+Tensor3D = np.ndarray[Tuple[int, int, int], np.dtype[T_dtype_co]]
+Tensor4D = np.ndarray[Tuple[int, int, int, int], np.dtype[T_dtype_co]]
+Tensor5D = np.ndarray[Tuple[int, int, int, int, int], np.dtype[T_dtype_co]]
+Tensor6D = np.ndarray[Tuple[int, int, int, int, int, int], np.dtype[T_dtype_co]]
+Tensor = np.ndarray[Tuple[int, ...], np.dtype[T_dtype_co]]