Technology
Vector Databases
Store and query vector embeddings for semantic search and AI applications. We use vector databases like Pinecone and Weaviate for RAG systems and recommendation engines.
Best For
Ideal use cases
Applications requiring semantic search
RAG (Retrieval Augmented Generation) systems
Recommendation engines
Similarity search applications
Projects with large embedding datasets
What We Build
Projects we deliver
RAG systems for chatbots
Semantic search applications
Document similarity systems
Recommendation engines
Content discovery platforms
Question-answering systems
Knowledge bases with search
Personalization systems
Ecosystem
Compatible tools & integrations
Seamless Integrations
Works with your existing stack
Use Cases
Recommended use cases
Chatbots needing context from documents
E-commerce product recommendations
Content platforms with semantic search
Knowledge bases with question-answering
Applications requiring similarity search
Delivery
How we deliver
We design vector database schemas for optimal query performance
Implement proper embedding generation and storage
Set up hybrid search (vector + keyword) when needed
Optimize index configuration and query parameters
Implement proper data synchronization and updates
FAQ
Frequently asked questions
Pinecone is best for managed, production-ready solutions. Weaviate offers open-source flexibility. pgvector works well if you're already using PostgreSQL. We recommend based on your requirements.
We use OpenAI's embeddings API, sentence-transformers, or other embedding models. We choose models based on your data type and language. Embeddings are generated during indexing and stored in the vector database.
Yes. We can self-host Weaviate, Qdrant, or use pgvector with PostgreSQL. Self-hosting offers more control but requires infrastructure management. Managed services like Pinecone simplify operations.
AI
Add AI on top of this stack
Two common AI services that pair well with this technology, plus a fixed-scope gig to start quickly.
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