AI tools that enable semantic search, vector similarity matching, and retrieval-augmented generation across large document collections.
14
Use Cases
20
Tools
14
Companies
Search & Vector Database Tools
AI-powered search feature by ServiceNow for surfacing relevant knowledge articles and answers instantly.
Search and discovery platform by Algolia offering fast, relevance-tuned search APIs for websites and apps.
Managed search and analytics engine for log analytics, application monitoring, and RAG knowledge bases.
Sparse keyword retrieval algorithm used in search systems for lexical matching alongside dense embeddings.
Open-source RAG framework by Pinecone Systems for building production-grade retrieval pipelines.
Natural language search tool by DISCO for querying legal documents using plain-English questions.
Reranking model by Cohere that improves search relevance by re-scoring retrieved document results.
Convolutional neural network architecture for generating image embeddings used in visual similarity search.
Search and analytics engine by Elastic offering full-text, vector, and hybrid search capabilities.
In-house embedding and facial similarity service by Chipper Cash for biometric fraud detection.
Open-source vector search library by Meta for efficient nearest-neighbor searches in embedding space.
Enterprise search platform by Glean connecting company knowledge across SaaS apps using AI.
PostgreSQL extension enabling vector similarity search directly within relational database workloads.
Managed vector database by Pinecone for real-time semantic search and similarity matching at scale.
Pod-based vector database by Pinecone for semantic search and similarity queries on large datasets.
Serverless vector database by Pinecone offering scalable semantic search without infrastructure management.
AI architecture pattern combining retrieval systems with language models to ground responses in data.
In-memory data store by Redis used as a vector database for semantic similarity search in AI apps.
AI retrieval pattern combining language models with document retrieval for grounded, accurate answers.
Knowledge graph component by Writer for connecting enterprise data to power grounded AI responses.
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Use Cases (14)
TaskUs is a leading outsourced digital services company providing next-generation customer experience (CX) for innovative global brands. To move beyond flat-file embedding storage and scaling limitations, TaskUs built TaskGPT—a proprietary GenAI platform—with Pinecone as the core vector database for semantic search, RAG-based knowledge retrieval, and client-specific recommendations. The result: a 20% reduction in average handle time and a 5% increase in customer satisfaction across client deployments.
Delphi is an AI platform that enables coaches, creators, and experts to deploy interactive “Digital Minds”—always-on conversational agents trained on their unique content. Scaling from proof of concept to a commercial platform with thousands of customers required a vector database that could support millions of isolated namespaces, billions of vectors, and sub-second retrieval under variable load. Delphi selected Pinecone, achieving P95 query latency of 100ms and keeping retrieval under 30% of total response time—freeing the engineering team to build product rather than manage infrastructure.
Gong is a revenue intelligence platform that analyzes billions of customer interactions to help sales teams improve performance. To power Smart Trackers—its patented AI system for detecting and classifying concepts in sales conversations—Gong adopted Pinecone as its core vector database, storing billions of sentence-level embeddings across real conversations. Migrating to Pinecone Serverless delivered a 10x reduction in infrastructure costs while sustaining peak search performance across a massive corpus.
Assembled is a workforce management and customer support optimization platform serving enterprises like Stripe, Etsy, and DoorDash. To power Assembled Assist, the company built a hybrid RAG pipeline combining Pinecone vector search with Algolia keyword retrieval and LLMs from OpenAI and Anthropic. Support tasks that previously took 40 minutes now complete in 2 minutes—a 95% reduction in handling time.
Notion, the connected workspace platform used by millions worldwide, integrated Cohere Rerank into its search pipeline to power Notion AI’s search accuracy across multilingual enterprise workspaces. Every search and Notion AI interaction now routes through Cohere Rerank, delivering dramatically improved relevance while cutting the cost and complexity of embedding-based retrieval for smaller workspaces.
Pure Storage, a Santa Clara-based enterprise data storage company, deployed Glean to unify knowledge access across Jira, GitHub, and internal wikis for teams spanning engineering, legal, and customer support. The AI-powered search platform cuts information-retrieval time by more than 30 minutes per search and enables employees to build custom GenAI applications in as little as 5 minutes, while boosting overall employee satisfaction scores by 39 points.
InpharmD's AI assistant, Sherlock, leverages Pinecone's vector database to deliver fast, accurate drug information to healthcare professionals. By embedding 30 million medical documents into a RAG pipeline, InpharmD achieved 70% better query accuracy, 95x faster first response times, and 80% cost savings on data storage.
1up, a sales knowledge automation platform, integrated Pinecone's vector database to power a RAG-based system that delivers real-time, highly accurate answers to complex sales queries. The solution replaced a slow, home-grown embedding system and achieved 10x faster response generation for RFPs and compliance questionnaires. Sales reps can now handle high volumes of queries with confidence, reducing reliance on colleagues and accelerating the go-to-market process.
Vanguard partnered with Pinecone to build Agent Assist, an internal RAG-powered AI chat tool that helps customer support representatives find answers faster and more accurately. By replacing keyword-based search with hybrid vector retrieval, Vanguard achieved 12% more accurate search results and meaningfully reduced call times — even during high-demand periods like tax season.
Chipper Cash, a fintech serving over five million customers across Africa, deployed a Pinecone-powered facial similarity search system to detect and block fraudulent duplicate sign-ups in real time. The solution slashed identity verification latency from up to 20 minutes down to under 2 seconds, and reduced fraudulent sign-ups by 10x across all markets.
The United Network for Organ Sharing (UNOS) leverages the ServiceNow AI Platform to coordinate thousands of organ transplants across the U.S. every year. By centralizing case management, enabling self-service portals, and deploying AI-powered IT operations, UNOS has quadrupled its case management capacity while managing nearly 300,000 support cases since 2018. The platform helps UNOS fulfill its 24/7 mission of saving lives with greater speed, accuracy, and efficiency.
Grupo Falabella, one of Latin America's largest retailers, deployed Salesforce Agentforce on WhatsApp to autonomously handle customer service inquiries across seven countries. The AI agent resolves 60% of requests without human intervention, operates 24/7, and has driven a 3x increase in WhatsApp conversations in just three months — shifting customer support away from costly phone channels.
Thomson Reuters integrated Claude via Amazon Bedrock into its AI platform, CoCounsel, to make the expertise of 3,000+ subject matter experts and 150 years of authoritative content accessible to legal and tax professionals. The solution combines Retrieval-Augmented Generation (RAG) architecture with multi-model deployment to deliver comprehensive, accurate professional analysis. Early adopters report dramatic efficiency gains, with some estimating task time cut in half or more.
Blue Origin deployed 2,700+ AI agents with 70% company-wide adoption, achieving a 90% reduction in hardware development time using Amazon Bedrock.
Amazon OpenSearch