Pinecone Assistant

By indexing documents in Pinecone's vector database and combining retrieval with language model generation, the assistant produces answers that cite specific sources rather than hallucinating.

The platform simplifies the implementation of production-quality RAG handling document ingestion, chunking, embedding, storage, retrieval, and answer generation in an integrated system.

Teams building knowledge bases, documentation assistants, or internal question-answering tools get a complete, managed solution rather than assembling individual components and managing the infrastructure themselves.

AI engineers, enterprise teams deploying internal knowledge assistants, and developer tool companies building documentation search use Pinecone Assistant as the foundation for accurate, trustworthy AI responses over proprietary content.

The Pinecone infrastructure's proven scale and low-latency retrieval performance ensure that accuracy improvements from grounding don't come at the cost of response speed in production.