Despite the impressive generative capabilities of large language models (LLMs), their lack of grounded reasoning and susceptibility to hallucinations limit their reliability in structured domains such as chess. We present Caïssa AI, a neuro-symbolic chess agent that augments LLM-generated move commentary with symbolic reasoning, knowledge graph integration, and verification modules. Caïssa AI combines a fine-tuned chess-specific LLM with a Prolog-based rule engine encoding chess tactics and rules, along with a dynamically constructed Neo4j knowledge graph representing the current board state. This hybrid architecture enables the system to generate not only accurate move suggestions but also coherent, strategically grounded commentary. A LangGraph-based verification module cross-checks LLM outputs against symbolic logic to ensure consistency and correctness, effectively mitigating hallucinations. By aligning data-driven generation with formal domain knowledge, Caïssa AI enhances both trustworthiness and explainability. Our results demonstrate that this tight neuro-symbolic integration produces verifiable, high-quality commentary and serves as a generalizable blueprint for AI systems requiring real-time, interpretable decision support.