Connect enterprise LLMs to millions of unstructured corporate documents. Engineered with Qdrant, Pinecone, and pgvector embeddings for sub-50ms hybrid semantic retrieval.
Production deployments across Qdrant, Pinecone, Milvus, and PostgreSQL `pgvector` tuned for HNSW indexing and low RAM consumption.
Custom document ingestion pipelines converting complex PDFs, legal tables, and unformatted data into context-aware vector chunks.
Combining sparse BM25 lexical keyword matching with dense vector distance scoring to eliminate model hallucinations and achieve 99%+ context accuracy.
Talk directly to our lead AI engineers to evaluate vector database benchmarks and chunking strategies.
Talk to Lead AI Engineer