AI Chat That Actually Knows Your Product
Set up in minutes. Powered by advanced LLMs with retrieval-augmented generation. Every RAG response is grounded in your documentation from every source — Notion, Google Docs, and uploaded files — with citations available and confidence-guided responses.
Core Capabilities
LLM-Powered
Leverages advanced language models for nuanced, context-aware responses. Configurable creativity levels let you tune between precise and conversational.
Multi-Source Unification
Notion pages, Google Docs, and uploaded files are unified into a single RAG pipeline. One question searches across all your knowledge, regardless of where it lives.
Streaming Responses
Server-Sent Events deliver responses word-by-word for a real-time chat experience. No waiting for the full answer to generate.
Citation Verification
Citations shown when the answer is sourced from your knowledge. Clickable citation links let users verify the answer themselves. Citation style is configurable (inline or footer).
Confidence Scoring
A 0-1 confidence score on RAG-grounded responses. Low confidence automatically triggers escalation to your human support team.
Multi-Turn Context
Maintains conversation context across multiple turns. Follow-up questions are understood in the context of the full conversation.
Knowledge Gap Tracking
Questions the AI can't answer are logged and surfaced in analytics, ranked by frequency. Included on every plan.
Built for Reliability at Scale
Enterprise teams need more than a wrapper around GPT. Rovixal's chat engine is built on infrastructure designed for reliability, performance, and consistency.
Reliable Processing
Document ingestion and sync jobs are processed through async queues with retries, backoff, and dead-letter handling. Chat responses stream in real-time via SSE.
Embedding Cache
Frequently asked questions hit the embedding cache instead of re-computing vectors. This reduces latency and API costs for repeated or similar queries.
Authority + Freshness Re-Ranking
Semantic search results are re-ranked based on source authority level and document freshness before reaching the LLM — ensuring authoritative, up-to-date answers.
How the RAG Pipeline Works
Retrieval-Augmented Generation (RAG) is what makes Rovixal different from a basic GPT wrapper. Here's what happens on every message:
User asks a question
The customer types a question in the chat widget or via the API. The message enters the processing queue.
Semantic search across all sources
The question is converted to an embedding and searched against your indexed documentation from Notion, Google Docs, and uploaded files using vector semantic similarity.
Authority + freshness re-ranking
Search results are re-ranked based on source authority level (PRIMARY/SECONDARY/REFERENCE) and document freshness (CURRENT/AGING/STALE) to prioritize the most reliable content.
Answer generation
The LLM generates a response grounded in the retrieved content, with inline citations linking to source documents.
Citation verification
Each citation is verified to ensure the source actually supports the claim. If a source can’t be verified, it’s clearly flagged as unverified.
Confidence scoring + delivery
A confidence score is computed based on semantic similarity. Low-confidence responses are flagged for escalation. The response streams to the user via SSE.
Make It Sound Like Your Brand
Configure every aspect of your AI assistant's personality and behavior to match your brand.