Add alternative approach for model conversion

docs/docsgen/source/intro/converters.md

@@ -64,6 +64,9 @@ on numpy or scipy. The user must implement
 its transformer or predictor with ONNX primitives, whether or
 not it was implemented with numpy.
 
+## Alternatives
+One alternative for implementing ONNX export capability is to leverage standard protocols such as the [Array API standard](https://data-apis.org/array-api/latest/), which standardizes a common set of array operations. It enables code reuse across libraries like NumPy, JAX, PyTorch, CuPy and more. [ndonnx](https://github.com/Quantco/ndonnx) enables execution with an ONNX backend and instant ONNX export for Array API compliant code. This diminishes the need for dedicated converter library code. It also provides a convenient primitive for converter authors looking for a NumPy-like experience when constructing ONNX graphs.
+
 ## Opsets
 
 ONNX releases packages with version numbers like