Libraria
Build production-ready RAG applications and custom AI assistants from your documentation in minutes.

Transform your business knowledge into autonomous, high-conversion AI chat agents.
OnSite represents a significant shift in the 2026 AI landscape, moving beyond simple script-based bots to fully autonomous agents powered by Retrieval-Augmented Generation (RAG). Technically, the platform utilizes a sophisticated ingestion engine that crawls web architectures, parses disparate file formats (PDFs, DOCX, CSV), and converts them into high-dimensional vector embeddings stored in a managed vector database. This architecture allows for near-zero latency retrieval during inference, ensuring that responses are grounded in real-time business data. Its market position is defined by its 'No-Code' accessibility, enabling non-technical operators to deploy LLM-powered interfaces that maintain brand voice and strict guardrails. By 2026, OnSite has evolved to include native multi-modal capabilities and deep integration with CRM systems, positioning it as a middle-ware layer between static enterprise data and dynamic customer interactions. The platform's emphasis on data security and SOC2 compliance makes it a viable solution for mid-market and enterprise firms looking to reduce support overhead without sacrificing the nuance of human-like communication.
Scheduled intervals for the bot to re-index web pages, ensuring the vector database reflects recent CMS updates.
Build production-ready RAG applications and custom AI assistants from your documentation in minutes.
Transform your business knowledge into hyper-responsive AI chatbots in seconds.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses LLM logic to detect when a user is ready to buy and dynamically injects capture forms into the chat flow.
Every response includes clickable citations linking directly to the source document or URL used for generation.
Ability to process and describe images uploaded by users in the chat window (e.g., for technical support tickets).
Threshold-based triggers that alert a human agent when the AI detects frustration or high-value intent.
NLP layer that understands business-specific jargon and acronyms even if they aren't explicitly defined in docs.
Edge-deployed chat widgets ensure the interface loads in <200ms globally.
Support staff overwhelmed by repetitive documentation questions.
Registry Updated:2/7/2026
Users leaving the site because they can't find specific product details.
HR spends hours answering questions about leave policy and insurance.