Problem: While LLMs are evolving, enterprises often struggle to unlock their full potential due to fragmented internal data, lack of secure knowledge retrieval, outdated or inaccurate responses, and privacy concerns with third-party RAG platforms.
Our Secure RAG Engine intelligently and securely merges data from internal databases with responses from public LLMs, ensuring outputs are precise, contextually relevant, and fully compliant with industry regulations.
Our flexible routing system can be customized through either rule-based or advanced AI-driven configurations. It prioritizes efficiency, domain expertise, or stringent privacy standards, ensuring that every query meets organizational and regulatory requirements.
We design hybrid retrieval pipelines that combine vector search, keyword search, and structured knowledge to serve relevant content to your LLM, reducing hallucination and improving response quality.
We help you choose and deploy the right vector database (e.g., Weaviate, Pinecone, Qdrant, FAISS) to store embeddings securely and scalably—on your cloud or on-prem. Indexing strategies are fine-tuned for speed, accuracy, and cost.