Implementation of Pinecone, Milvus, or Qdrant with optimized high-dimensional embeddings.
Combining dense vector search with sparse keyword search (BM25) and Cohere Reranking for maximum accuracy.
Intelligent document parsing algorithms that maintain context across complex PDFs, tables, and codebases.
RAG systems that can route queries, perform multi-hop reasoning, and self-correct retrieval failures.
Document-level permissions (RBAC) ensuring users only retrieve information they are authorized to see.
Automated ETL pipelines that keep your vector database synced with your live Confluence, Jira, or SQL databases.
We do not use no-code wrappers. We build highly scalable, custom software using enterprise-grade infrastructure.
A RAG system that ingests thousands of SEC filings and earnings call transcripts, allowing analysts to ask complex financial queries with exact page citations.
A developer-facing AI that retrieves code snippets and API usage examples from massive, fragmented documentation repositories instantly.
Talk directly to our lead engineers. We audit your requirements, propose the exact architecture, and give you a transparent roadmap — all in one call.