Your documents stay on-premise. Only small query chunks are sent to frontier LLMs for best-in-class answers — never your source files.
Documents and embeddings stay local. Only query context reaches the cloud LLM.
Documents are embedded and indexed locally — source files never leave.
All vectors and document metadata stored on your servers.
Query matching and chunk extraction happen entirely on-premise.
Gemini, GPT-4, or Claude processes the query + retrieved chunks to generate answers.
Source documents and full embeddings never leave your infrastructure. Only small chunks are sent.
Leverage the latest from Google, OpenAI, and Anthropic for best-in-class comprehension and generation.
No GPU servers needed. Standard compute handles embeddings and retrieval; the cloud handles generation.
Simpler infrastructure requirements mean you can go live in days with minimal setup.
Scale query volume up or down without hardware changes. Pay for what you use.
Start hybrid, move to fully offline later. The local components are identical in both models.
Full transparency on what's sent and what stays.
High-performance multimodal model with excellent reasoning capabilities.
Industry-leading language model with strong analytical and coding abilities.
Advanced AI assistant known for nuance, safety, and long-context understanding.
Internal knowledge bases, codebase documentation, and engineering wikis with frontier-quality answers.
Research databases and proposal archives with best-in-class synthesis and summarization.
Content archives and editorial databases with fast, intelligent search and generation.
Get started quickly with minimal infrastructure, then scale or migrate to offline as you grow.
See how hybrid RAG keeps your documents local while delivering cloud-quality answers.
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