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    AIRAG systems, search, recommendations

    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.

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    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

    7+ supported
    Pinecone for managed vector database
    Weaviate for open-source vector search
    OpenAI Embeddings API
    LangChain for orchestration
    Chroma for local development
    Qdrant for self-hosted option
    pgvector for PostgreSQL integration

    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.

    AI Agent Development

    Agents that plan and take actions via safe tools and approvals.

    AI Guardrails & Safety

    Injection defenses, tool allowlists, PII controls, and safe fallbacks.

    AI Guardrails & Prompt Hardening (Gig)

    Hardening pass for prompts/tools with safer production behavior.

    Related

    Explore related technologies

    AI

    OpenAI

    GPT and DALL-E APIs

    Chatbots, content apps, AI features
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    AI

    RAG Systems

    Retrieval augmented generation

    Knowledge bases, chatbots, Q&A systems
    Explore
    Backend

    Node.js

    JavaScript runtime for servers

    APIs, real-time apps, microservices
    Explore
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