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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 13 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Search across 30+ connected data sources (Confluence, Slack, Notion, SharePoint, Google Drive, etc.) with a single query interface. Uses natural language understanding to find relevant information without requiring exact keyword matches.
Automatically filters search results based on user role and access permissions, ensuring employees only see documents they're authorized to access. Inherits permissions from source systems (Confluence spaces, SharePoint folders, etc.).
Surfaces relevant information from other teams and departments, enabling employees to discover institutional knowledge they might not know exists and reducing siloed information.
Tracks search queries, user behavior, and knowledge gaps to provide insights into what information employees are seeking and what documentation may be missing or outdated.
Provides API endpoints for programmatic access to the knowledge base, enabling integration with other enterprise applications, chatbots, and custom workflows.
Automatically crawls, indexes, and maintains synchronized copies of content from 30+ data sources including Confluence, Slack, Notion, SharePoint, Google Drive, and others. Keeps indexes updated as source content changes.
Understands nuanced, contextual questions and returns relevant answers rather than just keyword-matched results. Interprets intent behind questions to surface the most useful information.
Integrates with enterprise SSO providers (Okta, Azure AD, Google Workspace, etc.) to authenticate users and manage access without separate credentials.
+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 Dashworks at 51/100. Dashworks leads on ecosystem, while Pinecone is stronger on adoption and quality. Pinecone also has a free tier, making it more accessible.
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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