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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Searches across multiple disconnected data sources (Slack, Salesforce, wikis, cloud storage, etc.) from a single search interface. Eliminates the need to manually search each platform individually by aggregating results across the entire tech stack.
Learns individual user behavior, team dynamics, and work patterns to personalize search results over time. Results improve as the system observes which documents users interact with and how they search.
Enables multi-turn search conversations where users can refine queries, ask follow-up questions, and maintain context across multiple searches. Understands query refinements and context from previous searches.
Proactively recommends relevant documents, people, and information to users based on their role, behavior, and interests. Surfaces content users might not have explicitly searched for but would find valuable.
Automatically summarizes multiple search results and synthesizes information across documents to provide concise answers. Extracts key information from lengthy documents and presents it in digestible form.
Identifies and surfaces internal experts, team members, and their areas of expertise based on organizational data, communication patterns, and documented knowledge. Helps employees find the right person to ask for specific knowledge.
Searches and retrieves specific documents, files, and knowledge artifacts (wikis, guides, policies, reports) from across the organization. Returns relevant documents with context and snippets.
Provides new employees with rapid access to relevant company information, policies, procedures, and team context through intelligent search. Reduces time spent searching for basic onboarding materials.
+5 more capabilities
Store and automatically index high-dimensional vector embeddings in a managed, scalable database without manual infrastructure provisioning. The system handles index optimization and partitioning transparently.
Query stored vectors to find semantically similar items by computing distance metrics between query embeddings and indexed vectors. Returns ranked results based on relevance in sub-100ms latency.
Serve as the retrieval component in Retrieval-Augmented Generation pipelines, providing relevant context documents to language models for grounded responses.
Provide a generous free tier (1 pod, 100K vectors) enabling teams to build and test real applications before committing to paid plans.
Execute searches using both dense vector embeddings and sparse keyword-based vectors simultaneously, combining results to improve relevance by capturing both semantic and lexical similarity.
Filter vector search results based on metadata attributes (tags, categories, timestamps, custom fields) before or during similarity search, enabling faceted and conditional retrieval.
Insert, update, or replace multiple vectors and their metadata in a single batch operation, optimizing throughput for bulk data ingestion without individual API calls.
Pinecone scores higher at 71/100 vs Glean at 49/100. Glean leads on ecosystem, while Pinecone is stronger on adoption and quality.
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Partition vector data within a single index using namespaces, enabling logical separation of data (by user, tenant, or dataset) without creating separate indexes.
+4 more capabilities