Zilliz Skillv0.0.2

Use Zilliz to store and retrieve product knowledge base embeddings. When a user asks a question, perform a semantic search to find relevant documents, then feed them to a language model to generate answers. This reduces response time and improves accuracy.

Index product descriptions, images, and reviews as vectors in Zilliz. Enable users to search by natural language queries (e.g., 'comfortable running shoes under $100') to find the most relevant items, improving conversion rates.

Store user preference vectors and content metadata in Zilliz. Compare user vectors against content vectors to recommend movies, songs, or articles. Hybrid search can combine semantic similarity with metadata filters like genre or release date.

Encode transaction patterns as vectors and store in Zilliz. When a new transaction occurs, query for similar past transactions flagged as fraudulent. High-speed vector search enables real-time fraud scoring.

Convert medical images (X-rays, MRIs) into vector embeddings and index them. Doctors can upload an image to find similar cases with known diagnoses, aiding in decision support and reducing diagnostic errors.

Offer Zilliz Cloud as a fully managed service, charging customers based on cluster size, storage, and query volume. Provide free and serverless tiers to attract startups, then upsell to dedicated clusters for enterprise needs.

Integrate Zilliz into larger SaaS products (e.g., CRM, analytics) as the vector retrieval engine. Charge per query or as part of the main product's pricing tier. Enable customers to add semantic search without building infrastructure.

Provide backup, import, and export services around Zilliz clusters. Offer automated backup policies, bulk data import from cloud storage, and disaster recovery solutions. Charge based on data volume and frequency.