Our new company website is ready for an intelligence layer that greets every visitor with fast, personalized answers. I need Grok XAI wired into the site and paired with a Retrieval-Augmented Generation pipeline that can draw on our own customer data while still tapping the full power of the larger Grok model. The work breaks down into three core pieces. First, build the RAG pipeline: ingest our customer records (CRM exports, support tickets, FAQs), vectorize them, keep the index fresh, and expose it to Grok so replies stay factual and brand-consistent. Second, embed a chat-style component in the React front-end, pass user context for true personalization, and style it to match the site. Third, stand up the middleware—secure API layer, authentication, rate-limits, plus a simple toggle that lets me choose “private data only,” “Grok only,” or a blended mode. A response should cite sources pulled from our data when relevant and return in under three seconds on typical queries. I’m flexible on tools, though Python, FastAPI, LangChain, and a vector store such as Pinecone or Weaviate fit well with what we already use. Deliverables include the fully functioning integration in our Git repository, deployment scripts, brief documentation, and a short knowledge-transfer call so my in-house devs can maintain it. Outline your approach and timeline and we can get started right away.