— AI STACK RECOMMENDATION

AI Customer Knowledge Base with RAG

Scalable RAG system for instant, accurate customer support answers using vector search, LLM inference, and document processing.

Stays alive for 365 days after the last visit.

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Scalable RAG system for instant, accurate customer support answers using vector search, LLM inference, and document processing.

high confidence

Multimodal embeddings for semantic search across customer docs, FAQs, and support tickets. Supports 100+ languages for global support teams.

Managed vector database optimized for RAG at scale. Auto-scaling, metadata filtering, and serverless pricing fit startup growth patterns.

Fast, accurate LLM for generating support answers from retrieved context. Low hallucination rate critical for customer trust.

Extract structured data from PDFs, scanned docs, and forms in knowledge base. Handles tables and handwriting for comprehensive doc ingestion.

Framework to orchestrate RAG pipeline: document loading, chunking, embedding, retrieval, and LLM generation with built-in memory.

Low-code backend to expose RAG pipeline as REST API for web/mobile support interfaces. Serverless scaling without DevOps overhead.

1Set up Pinecone vector database and create namespace for customer docs.
2Use AWS Textract to extract and parse PDFs, FAQs, and support articles into structured text.
3Chunk documents (500-1000 tokens) and embed with Cohere Embed API, store vectors + metadata in Pinecone.
4Build RAG pipeline in LangChain: retrieve top-k similar docs from Pinecone, pass to Claude Sonnet with customer query.
5Deploy pipeline via BuildShip as serverless API endpoint.
6Connect to support chat interface (web, Slack, or custom).
7Monitor retrieval quality and add new docs to knowledge base as support tickets arrive.

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