Why Vector Databases?
Traditional databases search for keywords. Vector databases search for meaning. To build AI applications that truly understand your business context, your data must be transformed into high-dimensional vectors (embeddings).
Our Approach
- Infrastructure Selection: We help you choose and deploy the right vector store (Pinecone, ChromaDB, Weaviate, or pgvector) based on your scale and latency requirements.
- Embedding Strategy: Selecting the optimal embedding models (OpenAI, Cohere, HuggingFace) to represent your specific domain data accurately.
- Hybrid Search: Implementing architectures that combine keyword search (BM25) with semantic search for maximum retrieval accuracy.
