Managing storage consumed engineering bandwidth. Offloading infrastructure let the team focus on algorithms and reach #1 RAG accuracy.
A no-code SaaS platform enables businesses to build domain-specific GPTs integrated with private data sources like Google Drive and Notion for employee training and support.
Building a production-grade vector database in-house would have imposed heavy operational costs and diverted engineering bandwidth from core product...
“Pinecone lets us focus on innovation and delivering customer value through our RAG-as-a-Service – without getting bogged down with vector database issues. We trust Pinecone to provide the foundational infrastructure we rely on for accurate, production-grade vector retrieval at scale.”
No-code platform for building custom AI chatbots from business data.
Managed vector database for AI search, retrieval, and recommendation systems.
Related implementations across industries and use cases
Manual review of sensitive files took two days. AI agents now finish the work in one hour.
Engineers spent weeks manually sharding. A serverless RAG pipeline now auto-scales 100 million vectors.
Engineers struggled to link scattered data. Now, an automated system connects sources instantly, fueling accurate autonomous agents.
Engineers manually correlated alerts across systems. AI agents now diagnose issues and suggest fixes, cutting recovery time by 35%.
Minor edits required days of crew coordination. Now, staff use avatars to modify dialogue and translate languages instantly.
Lab supply orders were handwritten in notebooks. Digital ordering now takes seconds, saving 30,000 hours for research annually.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.