AI explainability and transparency platforms help organizations make model outputs understandable, auditable, and compliant with growing regulatory and governance requirements. As of 2025-12-24, enterprises deploying assistants, contact‑center automation, legal workflows, and search are under increasing pressure to show provenance, decision logs, and human‑in‑the‑loop controls — driven by sector rules, corporate risk programs, and customer trust expectations. This category spans several tool types: conversation- and contact-center platforms (Observe.AI) that must surface why an agent suggested an action and provide auto‑QA traceability; domain-specialized assistants (Harvey) used in law and professional services where evidence, vaulting, and source attribution matter; enterprise virtual‑agent platforms (IBM watsonx Assistant) that combine no‑code and developer workflows and need change‑history and governance hooks; productivity‑embedded copilots (Microsoft 365 Copilot) that operate over sensitive documents and require data lineage and explainable recommendations; answer engines (Perplexity AI) that return sourced, web‑grounded answers; no‑code agent builders with auditability (Cimba.AI); and LLM infrastructure providers (Cohere) that supply private, customizable models and embeddings used to control behavior and trace outputs. Current practice emphasizes integrated provenance (citations, RAG tracebacks), decision logs and model cards, auditable training/usage records, and runtime controls for human oversight. When comparing XAI platforms, prioritize features such as source grounding, query/result lineage, tamper‑proof audit trails, policy hooks, and model/configuration versioning. These capabilities map directly to AI Governance Tools, Regulatory Compliance Tools, and AI Security Governance needs, helping organizations operationalize explainability without overstating what models can guarantee.