Privacy-First Architecture with RAG
In the age of LLMs, data privacy is paramount. Our Secure Document Chatbot leverages Retrieval-Augmented Generation (RAG) to ensure your sensitive documents never leave your secure perimeter during inference processing. Unlike standard models that might train on your data, this architecture isolates your context.
We use a vector database with strict access controls, ensuring that queries only retrieve snippets relevant to the user's permissions. This creates a robust barrier against data leakage while maintaining the intelligence of state-of-the-art language models.
Editable Flow: Visualizing AI Logic
How it works
- Drag-and-drop interface for conversation nodes
- Conditional branching based on user intent
- Real-time testing of logic paths
Black-box AI is a risk for enterprise. Our "Editable Flow" feature allows domain experts to map out exactly how the bot should behave in specific scenarios. You can visually inspect the decision tree, force specific responses for compliance, and let the AI handle the creative bridging between nodes.