Retrieving vector embeddings from petabytes of images, videos or text is challenging to scale, while having full control of its quality and a high precision in search system.
Quasara helps managing your ingestion pipeline to retrieve high-quality vector embeddings using the latest embedding models. We want to enable our customers to build a new wave of enterprise applications for data exploration, RAG, deep memories or real-time analytics; and reduce time-to-market for AI applications.
Hello Everyone!
We are excited to launch the newest version of our product: Vectorisation as a a Service
FEATURES include:
- Vectorisation of images
- Vectorisation of videos
- Simple and complex vector tailor-made to your use case
- Provision of vector embeddings via download or API
- Additional options via API
- Semantic search with natural language or images
- Clustering search
- Similarity search
- Outlier search
WHAT'S NEXT?
- Vector streaming of large amounts of vectors
- Enriching vectors with additional metadata
Get a Free Trial on our website now :)
this looks pretty interesting and maybe something we could combine with Tilores for RAG. good luck with the launch!
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Maker
@major_grooves thank you so much Steven. Let's discuss how we could leverage the scattered customer data you unify with the vectorisation capabilities of Synapsis. Looking forward to the chat!
wow if that final example works (blond woman wearing a blue hermes dress...) then that's insane retrieval!
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Maker
@jonnymiles It does work indeed. Very complex descriptions of scenarios (for autonomous driving) or any other use case of visual data taken by satellites, CCTV cameras, dash cams, drones, endoscopic cameras become possible to retrieve.
That makes vector embeddings so powerful to use in many applications.
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Tilores entity resolution
PicLooks Avatars