Next-Gen Support: Reducing Churn with Retrieval-Augmented Generation (RAG) ​

The Challenge: SaaS platform with complex docs faced high churn (25% monthly). Users frustrated by slow searches; support swamped with repetitive Level 1 queries (70% volume). Generic bots hallucinated 40%+ time, eroding trust. The Solution: Specialized Python/LangChain/Pinecone RAG assistant. Knowledge Ingestion: ETL pipeline (BeautifulSoup + Unstructured.io) scrapes PDFs/Notion/help center, chunks to 512-token embeds (OpenAI text-embedding-3-large), upserts to Pinecone index (hybrid dense/sparse search). Contextual Retrieval: User query → top-5 chunks (similarity >0.8), injected to GPT-4o prompt with source links. FastAPI Backend: Async endpoints (<500ms latency) embed in Intercom widget; fallback to human at <70% confidence. Multilingual via auto-detect. Implementation Details: Hosted on Vercel; retrains embeddings weekly via cron. Tracks CES/NPS in-app. ​ The Results: Resolves 65% queries autonomously (95% FRT drop), 4hrs→sub-2s responses. 90% repetitive ticket reduction; deflection rate 65%, aligning with 45% industry resolution gains. Agents now handle complex escalations, cutting churn 20%.