Core Competency
Vector Databases for LLMs
Storing and querying high-dimensional vectors to power Retrieval-Augmented Generation (RAG), semantic search, and next-generation AI enterprise applications.
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Semantic Search
Moving beyond rigid keyword matching to return highly contextual results based on the actual meaning and intent behind a user's query.
Enterprise RAG
Grounding conversational AI agents in your proprietary, highly classified documents to prevent LLM hallucinations and ensure accurate answers.
High-Dimensional Indexing
Utilizing advanced algorithms like HNSW to query millions of vector embeddings with sub-millisecond latency.