How Perplexity Built an AI Google

Perplexity AI built an answer engine that combines real-time web search with large language models through a Retrieval-Augmented Generation (RAG) pipeline. The architecture uses Vespa AI for web-scale indexing and retrieval across 200 billion URLs, a model-agnostic orchestration layer that routes queries to appropriate LLMs (both proprietary Sonar models and third-party models like GPT and Claude), and a custom ROSE inference engine running on NVIDIA H100 GPUs. The system processes queries through five stages: intent parsing, live web retrieval, snippet extraction, answer generation with citations, and conversational refinement. This approach addresses AI hallucination issues by grounding responses in verifiable sources while maintaining low latency and cost efficiency through intelligent model routing and infrastructure optimization.