### Answer Based on the available sources, here are some customers using Pinecone for semantic search: Stravito - A platform that helps product creators and marketers store, discover, and integrate consumer insights [(1) ]() . They use Pinecone's semantic search capabilities to power Stravito Assistant, which enables users to find answers faster and ask more targeted questions about market and consumer data . The platform automatically categorizes data using machine learning and offers a natural language query interface to break down data silos . Frontier Medicines - They use Pinecone for next-generation vector embedded molecule search in drug discovery [(2) ]() . According to their Chief Technology Officer Johannes Hermann PhD, "The introduction of Pinecone serverless has led to amazing performance and efficiency improvements in our vector search capability. We will continue to push forward searching Billions of vectors with Pinecone serverless at the center" . The sources also mention several use case examples where companies leverage Pinecone for semantic search [(3) ]() : - A leading telecom company uses Pinecone to enable their customer service teams to search through their internal knowledge base to respond to customer inquiries quicker and with more accuracy - A global ERP software company uses Pinecone to let their customers get insights from employee feedback using semantic search - An online-learning company uses Pinecone to power their core search and question-answering feature for millions of users

​ To learn more, see the sources I considered: [[1] Stravito turns market and consumer data into actionable insights with Pinecone Inference | Pinecone](https://www.pinecone.io/customers/stravito) [[2] Semantic Search | Pinecone](https://www.pinecone.io/solutions/semantic) [[3] Semantic search with Pinecone | Pinecone](https://www.pinecone.io/learn/search-with-pinecone) ​

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### Answer Based on the available sources, here are examples of customers and use cases for agents with Pinecone: Amazon Bedrock Integration - Pinecone serves as a Knowledge Base for Amazon Bedrock, where users interact with Agents that combine natural language interfaces with Knowledge Bases [(1) ]() . These agents use Pinecone to store embeddings and retrieve semantically relevant content when responding to user queries . Research Agents - The sources describe how to build research agents using Pinecone with LangGraph [(2) ]() . Research agents are multi-step AI agents that provide in-depth answers by searching through multiple data sources, including knowledge bases stored in Pinecone . These agents can search the web, read papers, and access other relevant sources of information for their particular use case . Legal Discovery and Analysis - Legal professionals use Pinecone-powered agents for legal research and analysis [(3) ]() . These applications can analyze hundreds of contracts for specific clauses in minutes instead of days, and find exact legal precedents in seconds . The sources also mention that agents can be built using various frameworks: - LangChain agents - Popular for building ReAct-style agents that use reasoning and action steps [(4) ]() - LangGraph agents - For more sophisticated, graph-based agent construction with fine-grained control [(2) ]() - Amazon Bedrock Agents - That integrate with Pinecone as the vector database backend [(1) ]() These agents leverage Pinecone's vector database capabilities to provid

e long-term memory, context retrieval, and knowledge-based decision making for various AI applications [(2) ]() [(4) ]() .

​ To learn more, see the sources I considered: [[1] Pinecone as a Knowledge Base for Amazon Bedrock | Pinecone](https://www.pinecone.io/blog/amazon-bedrock-integration) [[2] LangGraph and Research Agents | Pinecone](https://www.pinecone.io/learn/langgraph-research-agent) [[3] Accelerating Legal Discovery and Analysis with Pinecone and Voyage AI | Pinecone](https://www.pinecone.io/learn/legal-semantic-search) [[4] LLMs Are Not All You Need | Pinecone](https://www.pinecone.io/learn/llm-ecosystem) ​

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