AI-powered brain–computer interface (BCI) platforms bring together model lifecycle tools, multimodal data stores, GPU orchestration, and agent frameworks to support neural decoding, closed‑loop control, and product‑grade deployment. As of 2026, improvements in neural sensors, real‑time inference, and foundation models have pushed BCI from lab prototypes toward clinical and industrial pilots, increasing demand for reproducible datasets, low‑latency inference, and scalable training pipelines. Core capabilities map to the two categories listed: AI Tool Marketplaces (for model discovery, fine‑tuning, and deployment artifacts) and AI Data Platforms (for versioned, indexed multimodal datasets and vector search). Example components: Vertex AI provides an end‑to‑end managed stack for training, fine‑tuning, evaluating, and deploying models; Run:ai supplies Kubernetes‑native GPU pooling and orchestration for large‑scale model training and mixed cloud/on‑prem workflows; Activeloop Deep Lake offers a multimodal database to store, version, stream, and index neural signals, video, and sensor telemetry; OpenPipe captures interaction logs and supports fine‑tuning and evaluation workflows. LangChain and LlamaIndex enable agentic control layers and retrieval‑augmented workflows for mapping model outputs to device control, safety checks, and human‑in‑the‑loop prompts. Warp’s Agentic Development Environment accelerates developer testing and iteration of agentic BCI flows. Key considerations for BCI platforms include latency and edge/cloud partitioning, rigorous dataset versioning and provenance, privacy and regulatory compliance, real‑time evaluation metrics, and reproducible fine‑tuning/serving pipelines. Combining marketplaces for vetted models with data platforms that handle multimodal neural data is becoming a practical foundation for moving BCIs out of isolated research projects and into regulated, production environments.