This category covers the platforms and frameworks enterprises use to build, deploy and govern predictive analytics and machine‑learning systems for forecasting, scenario planning and competitive intelligence. By 2026 organizations are integrating foundation models, time‑series ML, and multi‑agent workflows into forecasting stacks while demanding explainability, observability and strict data governance. Key trends include broader adoption of no‑code/low‑code model builders for business users, the rise of open‑source instruction models for fine‑tuning, and purpose‑built clouds that accelerate training and serverless inference. Representative tools: StackAI and IBM watsonx Assistant provide end‑to‑end, enterprise‑focused platforms for building and operationalizing AI agents and forecasting pipelines with governance and low‑code options; Kore.ai specializes in orchestrating multi‑agent workflows with observability and enterprise security controls; LangChain offers engineering frameworks to build, test and deploy reliable agentic ML applications and stateful workflows; Together AI supplies GPU‑backed cloud services for fast fine‑tuning and high‑throughput inference of forecasting models; and open families like nlpxucan/WizardLM enable organizations to fine‑tune instruction‑following LLMs for domain‑specific forecasting, reasoning and data augmentation. For data analytics, market and competitive intelligence teams this ecosystem means faster model iteration, tighter integration between signal ingestion and forecasting, and more explicit controls for monitoring and compliance. When evaluating platforms, prioritize capabilities for time‑series support, explainability, model governance, scalable inference, and integration with data pipelines—criteria that reflect enterprise needs in 2026 for reliable, auditable forecasting workflows.