This topic covers the design and deployment of verifiable, fact‑driven AI assistants for trading — systems that combine live market data, retrieval‑augmented generation (RAG), provenance-aware answers, and enterprise model governance to produce audit‑ready signals and explanations. Drawing on the capabilities of modern toolchains and prevailing industry trends (no related articles were provided), these assistants use web‑grounded answer engines, vector stores, private LLMs, managed model platforms, and dataops services to reduce hallucinations and support compliance. Key building blocks include: Perplexity AI for real‑time, cited, web‑grounded answers; Pinecone as a low‑latency vector database for semantic search and RAG; Cohere for private, customizable LLMs and embeddings; Google’s Vertex AI and Gemini for model training, multimodal inference, and scalable deployment; Scale AI for high‑quality labeled data, RLHF, evaluation, and safety workflows; and integrations such as Microsoft 365 Copilot to embed insights into trader workflows. Relevance and timeliness (as of 2026‑01‑07) stem from stronger regulatory and institutional demands for explainability, provenance, and model risk management in financial services, plus increasing adoption of RAG and vector DBs to ground generative answers with verifiable sources. Practical implementations prioritize streaming data feeds, citation tracing, human‑in‑the‑loop verification, model‑performance monitoring, and secure private models. Organizations evaluating AI trading assistants should weigh latency, data provenance, retrievability, model governance and the quality pipeline for training/evaluation to ensure outputs are both actionable and auditable.