02
Case 02RAGChat + Human Handoff

SAASPRODUCT SUPPORT

Documentation-trained AI with seamless human escalation

RAGChatHuman Handoff
Systems:2
Journey Steps:6
Tech Components:8

The Problem

Users struggle with setup while support teams repeat themselves

01

Product documentation is vast — users can't find the specific answer they need

02

Tier 1 support agents spend most of their time on questions already answered in docs

03

Complex, account-specific issues need human expertise but there's no smooth transition from bot to agent

04

No visibility into what questions customers ask most or where documentation has gaps

The Solution

AI assistant trained on your product docs with human handoff

An AI support agent that ingests your full documentation — API references, setup guides, changelogs — and handles routine questions. When a user needs account-specific help or hits a complex issue, the conversation seamlessly transfers to a human agent with full context preserved.

System Capabilities

[01]Ingests product docs, API references, and changelogs
[02]Streaming responses for real-time interaction
[03]Automatic human handoff for complex issues
[04]Agent receives full conversation history and context
[05]Knowledge gap detection highlights doc deficiencies
[06]Intent classification (FAQ, support, escalation)

User Journey

Step-by-step flow from initial contact to resolution

01

User asks a setup question

RAGChat

"How do I configure SSO with our identity provider?" — sent via embedded chat widget

02

Documentation searched

RAGChat

RAG retrieves relevant sections from SSO setup guide and API docs

03

Step-by-step response generated

RAGChat

LLM produces a walkthrough with citations to specific doc pages

04

User has a follow-up issue

RAGChat

"I followed the steps but I'm getting a SAML assertion error with our Okta setup"

05

Handoff triggered

Human Handoff

Intent classified as account-specific support — conversation routed to human agent

06

Agent picks up with context

Human Handoff

Support agent receives full conversation transcript, user context, and quality score

Pipeline Complete — 6 Steps

System Architecture

How data flows through the system for this use case

system-architecture.flow
N01

User

Embedded chat widget

N02

RAG Pipeline

Semantic search over product docs

N03

LLM Engine

Citation-backed responses

N04

Handoff Engine

Intent-based escalation

N05

Agent Inbox

Full context + transcript

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Routine questions are handled end-to-end by the RAG pipeline. When intent classification detects a need for human expertise, the handoff engine transfers the conversation to the agent inbox with full context preserved.

Tech Stack

Technologies powering this solution

01FastAPI
02Pinecone
03Groq / OpenAI
04WebSocket
05Human Handoff
06Intent Classification
07Redis
08PostgreSQL
Ready to Deploy

Deploy This Solution

Custom-built for your specific documents, workflows, and channels.

SAAS PRODUCT SUPPORT — Fenlo AI Use Cases | Fenlo AI