Field techs waited 24s for repair guidance. Vector retrieval cut response times to 2.89s and reduced parts costs by 62%.
An AI platform provider for the heavy machinery and medical equipment industries aggregates vast repositories of technical data, including manuals, schematics, and technician notes.
An in-house search system built on PostgreSQL and blob storage was too slow for real-time needs, with full responses taking approximately 24 seconds....
“Pinecone is a critical part of our agentic architecture; it powers the retrieval backbone of Aquant AI, including our knowledge agent, which delivers real-time, context-aware guidance to service professionals. Its performance and scalability allow us to serve our customers in production at enterprise scale, without compromising speed or accuracy. That’s enabled us to move beyond static answers and toward dynamic, AI-driven service intelligence.”
Agentic AI platform for servicing and maintaining complex industrial equipment.
Managed vector database for AI search, retrieval, and recommendation systems.
Related implementations across industries and use cases
Digging through massive PDFs took minutes. A governed RAG pipeline now retrieves technical answers in seconds.
Analysts stitched insights from scattered PDFs. AI now retrieves buried data instantly, cutting memo creation from 2 days to 0.5.
Agents lost 30 minutes scouring 14,000 PDFs. Gen AI now pinpoints repair steps instantly, saving clients up to $12k/hr in downtime.
Patent strategy relied on subjective manual judgment. AI now standardizes analysis, cutting research cycles from months to days.
IT staff logged in every six hours to check 30 systems. GenAI now automates monitoring, saving 3,000 hours and ending downtime risk.
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.