Agentic RAG-Powered Support Assistant

Overview

An agentic RAG-powered support assistant that autonomously resolves technical product queries using LLM-driven reasoning, replacing rigid rule-based responses with retrieval-augmented, context-aware answers generated in real time from relevant product knowledge.

Purpose — Why This Project Exists

Rule-based support bots break the moment a question doesn't match a pre-written pattern, forcing users into frustrating dead ends or a support queue. This assistant exists to replace that brittle logic with an agent that actually retrieves the relevant product knowledge for a query and reasons over it — giving real-time, knowledge-grounded answers instead of a canned response.

Animated Workflow

1

User Query

A technical product question comes in.

2

Intent Understanding

LLM interprets what's actually being asked.

3

Embedding & Search

Query embedded and matched via semantic search.

4

Knowledge Retrieval

Relevant product knowledge pulled via RAG.

5

Response Generation

LLM composes a grounded, context-aware answer.

6

Grounded Answer

Delivered to the user in real time.

Key Features

Agentic Reasoning — LLM-driven reasoning autonomously resolves technical queries.

Retrieval-Augmented Responses — context-aware generation replaces rule-based query handling.

Embedding & Semantic Search — pipeline built for real-time, knowledge-grounded conversations.

Real-Time Support — automated response-generation pipeline running live.

Benefits

Fewer Escalations

Resolves queries directly instead of routing everything to a human queue.

Always-On

Answers technical questions in real time, any time.

Grounded, Not Generic

Every answer traces back to real product knowledge, not a canned script.

Technology Stack

LangChain Python Vector Search Embeddings Large Language Models

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