This topic covers how modern AI stacks are used to extract, aggregate and analyze crypto sentiment signals—and how those signals feed prediction markets and forecasting workflows. By 2026, practitioners increasingly combine PDF automation and document‑agent tooling with vector search, retrieval‑augmented generation (RAG) and enterprise analytics to create repeatable pipelines for social, on‑chain and research data. Key building blocks include LLM engineering frameworks (e.g., LangChain) for agent orchestration and workflow automation; document/RAG platforms (LlamaIndex) and PDF automation APIs to ingest whitepapers, reports and filings; production vector stores (Pinecone) for fast semantic retrieval; and analytics/BI platforms (Alteryx, Sisense, Domo) to transform signals into dashboards, backtests and governed deployments. Specialist data engines (Altss, Edwyn) and CX/sentiment platforms add domain feeds such as investor intelligence and structured SEC data. Practically, these components let teams convert raw text, forum posts, on‑chain metrics and PDFs into vectorized embeddings, surface relevant context via RAG, and feed signal aggregates into models or market mechanisms (prediction markets, hedging strategies). Important considerations include latency for real‑time markets, model and data governance, explainability of sentiment scores, and regulatory/compliance constraints around market signals and trading. This topic is timely because increased tokenization, volatile macro cycles and broader adoption of LLMs have raised demand for automated, auditable sentiment pipelines that support both exploratory research and operational forecasts. The emphasis is on combining robust infrastructure (vector DBs, agents, BI) with disciplined analytics rather than on one‑off models or ungoverned signals.