docs: connect Hub visitors to the live ArtifactNet demo

README.md

@@ -14,6 +14,8 @@ pipeline_tag: audio-classification
 
 ArtifactNet detects AI-generated music by extracting forensic residual artifacts via a task-specific UNet, rather than learning generator-specific patterns. This approach generalizes across 22 AI music generators with only 4.2M parameters.
 
+**Try it without installing anything:** [analyze a track in the live ArtifactNet demo](https://demo.intrect.io/?utm_source=huggingface&utm_medium=referral&utm_campaign=artifactnet_hub_2026q3&utm_content=model_card). For the production API, batch workflow, limitations, and current plans, see the [ArtifactNet product page](https://intrect.io/artifactnet/?utm_source=huggingface&utm_medium=referral&utm_campaign=artifactnet_hub_2026q3&utm_content=model_card).
+
 > ⚠️ **License: CC BY-NC 4.0 — Non-Commercial Only**
 > This ONNX inference build may not be used for any commercial product, service, API, or
 > revenue-generating activity. Research, academic, and personal evaluation use are welcome.
@@ -80,6 +82,8 @@ For song-level verdict, compute median over multiple chunks.
 
 Evaluate with [ArtifactBench v1](https://huggingface.co/datasets/intrect/artifactbench).
 
+If you want to compare the public ONNX build with the production detector on your own audio, use the [live demo](https://demo.intrect.io/?utm_source=huggingface&utm_medium=referral&utm_campaign=artifactnet_hub_2026q3&utm_content=model_benchmark).
+
 ## Citation
 
 ```bibtex