pretty relevant # Explain any model with one line of code See what your model actually learned — not what you hoped it learned. 40+ methods across PyTorch, TensorFlow, and JAX. Open source. ## See what AI sees Real attribution results from xaitalk validation runs. DistilBERT · gradient_x_input · PyTorch ## Same API. Different frameworks. xaitalk detects your framework automatically. The unified API stays the same — the native implementation underneath changes per framework. Switch framework to see what runs under the hood. ``` import torch import xaitalk result = xaitalk.explain(model, x, method="gradient", target_class=0) ``` ``` # What happens internally: model.eval() x = x.detach().clone().requires_grad_(True) output = model(x) target = torch.zeros_like(output) target[0, 0] = 1.0 output.backward(gradient=target) attribution = x.grad.detach().cpu().numpy() ``` ## XAI belongs in production, not just in papers Explainability has been a research topic for years. xaitalk makes it a practical tool — so you can actually verify, debug, and trust your AI before it reaches users. ### Shadow models for closed-source APIs Train an open model to replicate a closed-source API on your specific workflow — email classification, content moderation, credit scoring. Then explain every prediction. Replace black-box costs with transparent decisions. ### Verify learning during training Run attributions at checkpoints to see what your model focuses on at each epoch. Catch Clever Hans moments early — is it learning the feature or the artifact? Compare methods to build confidence before deployment. ### Fairness with counterfactual testing Would the credit decision change if the applicant's gender were different? xaitalk's counterfactual fairness method flips protected attributes and compares attributions — concrete evidence for auditors and regulators. ### Give stakeholders real instruments Doctors reviewing AI-flagged scans, loan officers explaining rejections, compliance teams auditing automated decisions — xaitalk turns model outputs into visual evidence that non-technical people can evaluate. ### Any input dimension. Any architecture. CNNs, Transformers, LLMs, GNNs, RNNs, diffusion models, protein folders, game engines. 0D Tabular Credit scoring, features 1D Sequences ECG, audio, text 2D Images X-rays, photos, scans 3D Video Surveillance, sports 4D Spatiotemporal Chess, V-JEPA ## Start explaining your models Open source library. Cloud API. Integration services. ### Open Source In development — not yet released ### Cloud API GPU-powered inference pretty relevant ### Product ### Resources ### Legal © 2026 xaitalk · A product of 7R+ GmbH pretty relevant © 2026 xaitalk · A product of 7R+ GmbH --- Human-readable version: https://xaitalk.com/