Our clients
How our visual search technology works
Find similar images by similar vectors
Visual search AI models represent or “embed” images with their “fingerprints” - series of numbers called vectors that capture the most essential features of an image. Those fingerprint vectors are created in such a way that similar images have similar fingerprints and are geometrically clustered together. This embedding trick allows the search engine to quickly find similar images by their vector representation.
Full-fledged image recognition pipelines
Our computer vision models perform all tasks required for successful visual search - from object detection, to segmentation, to vector representation and vector search of the target image.
Best tools for the job
Our visual search technology utilizes the most recent advancements in computer vision powered by deep learning algorithms. We custom-select, modify, ensemble, train, and fine-tune state-of-the-art visual models for a particular task.
Collect and organize domain specific data
We train our models on all available data related to the task - both customer-specific and publicly available. We perform data cleaning and labeling using advanced unsupervised and semi-supervised techniques to build large scale datasets to achieve the best search results.
Accelerate implementation with our visual search blueprint
- Accurate results - up to 98 percent item identification accuracy.
- Advanced similarity - the AI model takes into account fashion, decor, and artistic style.
- High throughput - battle-tested architecture handling thousands of parallel searches.
- Low latency - low latency with optimized vectorizers and fast approximate nearest neighbor search.
- Highly scalable and robust - share-nothing microservices architecture ensures high scalability and resilience.
- Integrations - data consumption from message queues, databases, or file dumps and REST APIs ensure seamless integration with the rest of the ecosystem.
- Infrastructure -AWS, GCP, or Microsoft Azure are supported. On-prem solution is available as well.
- Deep learning: A choice ofTensorFlow or Pytorch
- Vector/ANN index: Milvus or Elasticsearch, as well as embedded implementations
- Data platform: A choice of Apache Spark, Apache Flink, or Apache Beam are the primary choices along with their cloud wrappers.
- Feature store: Feast is one option, yet many non-specialized databases and EDW solutions will work
- Infrastructure -AWS, GCP, or Microsoft Azure are supported. On-prem solution is available as well.
- Deep learning: A choice ofTensorFlow or Pytorch
- Vector/ANN index: Milvus or Elasticsearch, as well as embedded implementations
- Data platform: A choice of Apache Spark, Apache Flink, or Apache Beam are the primary choices along with their cloud wrappers.
- Feature store: Feast is one option, yet many non-specialized databases and EDW solutions will work
Read more about our visual search case studies
Get started with visual search
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