AI-powered product support across website chat and WhatsApp
Support teams buried in repetitive product questions
Customers ask the same questions about sizing, shipping, and returns — over and over
After-hours inquiries go unanswered, leading to abandoned carts
Product info is scattered across PDFs, help docs, and internal wikis
Support agents spend most of their time on copy-paste answers instead of complex issues
AI assistant trained on your product catalog and policies
An AI chatbot that ingests your product documentation, FAQ pages, and return policies — then answers customer questions with accurate, citation-backed responses. Deployed on your website widget and WhatsApp so customers get instant help wherever they reach out.
Step-by-step flow from initial contact to resolution
"What's your return policy for electronics?" — sent via WhatsApp or website widget
Content filtered through prompt guard, then routed to RAG retrieval
Semantic search finds the most relevant sections from your return policy documents
LLM produces an accurate answer referencing the specific policy document and section
Sentiment analyzed, intent classified as FAQ, quality score assigned to the response
Answer sent back to the customer on the same channel they used — WhatsApp or widget
Full conversation stored with analytics metadata for dashboard review
How data flows through the system for this use case
WhatsApp or website chat widget
OmniBot multi-channel ingress
Semantic search over product KB
Groq primary, OpenAI failover
Cited answer via original channel
Messages from any channel enter the unified pipeline. RAG retrieval pulls relevant product documentation, the LLM generates a citation-backed response, and OmniBot delivers it back through the original channel.
Technologies powering this solution
Custom-built for your specific documents, workflows, and channels.