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    Vector Databases & Embeddings

    We design and implement semantic search systems and vector infrastructure that powers intelligent AI applications. From knowledge bases to recommendation engines at scale.

    Build Your Search System
    search/vector_store.py
    1import pinecone
    2from openai import OpenAI
    3 
    4def semantic_search(query: str, top_k=5):
    5 embedding = openai.embed(query)
    6 results = index.query(
    7 vector=embedding,
    8 top_k=top_k,
    9 include_metadata=True
    10 )
    11 return [r.metadata for r in results]
    12 # cosine similarity ✓
    What We Build

    Intelligent Search & Retrieval

    Vector Database Selection

    Pinecone, Weaviate, Qdrant, pgvector, Chroma. We evaluate your query patterns, scale, and latency needs to choose the right store.

    Embedding Model Integration

    OpenAI text-embedding-3, Cohere, BGE, and sentence transformers. We select and fine-tune the right embedding model for your domain.

    Semantic Search Systems

    Replace keyword search with meaning-based retrieval. Users find what they're looking for even when they don't know the exact terms.

    Hybrid Search

    Combine dense vector search with sparse BM25 for the best of both worlds. Semantic understanding plus keyword precision.

    Document Intelligence

    Intelligent document processing systems that understand context, extract entities, and answer questions about your document corpus.

    Embedding Pipelines

    Automated pipelines that ingest, chunk, embed, and index new content as it arrives. Keeping your vector store always up to date.

    Tech Stack

    Tools We Master

    Pinecone
    Weaviate
    Qdrant
    pgvector
    Chroma
    OpenAI Embeddings
    Cohere
    FAISS
    LangChain
    LlamaIndex
    sentence-transformers
    BM25

    Need Smarter Search?

    Let's architect your semantic search system.

    Book your Consultation