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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Identifies and classifies products in images using pre-trained deep learning models. Recognizes product categories, brands, and specific items without requiring custom model training.
Finds visually similar images from a database based on an input image query. Uses deep learning embeddings to match products by appearance rather than metadata.
Identifies duplicate and near-duplicate images in a product catalog, catching variations like different angles, lighting, or minor edits that basic pixel matching would miss.
Trains custom image recognition models tailored to specific product categories or visual attributes not covered by pre-trained models. Requires contacting sales for implementation.
Processes multiple images in bulk for recognition, similarity search, or duplicate detection. Optimized for handling large catalogs without individual API calls.
Automatically assigns tags and classifies images into predefined categories using computer vision. Enables rapid metadata generation without manual annotation.
Provides REST API endpoints for integrating visual search and image recognition into applications. Enables developers to embed Ximilar capabilities directly into their products.
Specialized image recognition for fashion items including clothing, accessories, and footwear. Identifies style, color, fit, and other fashion-specific attributes.
+2 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 Ximilar at 46/100.
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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