You will get AI Customer Support Agent (LLM + RAG)
Project details
Built an intelligent customer support agent that combines a Large Language Model with Retrieval-Augmented Generation (RAG) to deliver accurate, context-aware responses grounded in company-specific data.
Instead of relying solely on the LLM's general knowledge, the system retrieves relevant chunks from a custom knowledge base (FAQs, product docs, policies) using vector embeddings and semantic search, then feeds that context into the LLM to generate precise, hallucination-resistant answers.
Key components:
Document ingestion & chunking pipeline for knowledge base
Embedding generation and vector store for semantic retrieval
Intent classification to route queries before retrieval
Prompt engineering with few-shot examples for consistent tone
Fallback logic for out-of-scope or ambiguous queries
Tech stack: Python, OpenAI API, Vector Database, Prompt Engineering
Result: Significantly reduced irrelevant/hallucinated responses by grounding answers in real source documents rather than model memory alone — making it suitable for e-commerce, SaaS, or telecom support use cases.
Instead of relying solely on the LLM's general knowledge, the system retrieves relevant chunks from a custom knowledge base (FAQs, product docs, policies) using vector embeddings and semantic search, then feeds that context into the LLM to generate precise, hallucination-resistant answers.
Key components:
Document ingestion & chunking pipeline for knowledge base
Embedding generation and vector store for semantic retrieval
Intent classification to route queries before retrieval
Prompt engineering with few-shot examples for consistent tone
Fallback logic for out-of-scope or ambiguous queries
Tech stack: Python, OpenAI API, Vector Database, Prompt Engineering
Result: Significantly reduced irrelevant/hallucinated responses by grounding answers in real source documents rather than model memory alone — making it suitable for e-commerce, SaaS, or telecom support use cases.
Purpose
BusinessIndustry
Ecommerce, Education, Government & Public Sector, Logistics & Supply Chain Management, Marketing & Advertising, Telecommunications, Transportation & AutomotiveLanguage
EnglishWhat's included
| Service Tiers |
Starter
$65
|
Standard
$150
|
Advanced
$320
|
|---|---|---|---|
| Delivery Time | 1 day | 7 days | 20 days |
Number of Hours of Work | 8 | 18 | 35 |
Scriptwriting | |||
Summary Report | - | ||
Social Media Replies | |||
Email Support | |||
Live Chat Support | - |
About Tabish
AI Engineer
Faisalabad, Pakistan - 12:55 am local time
"Hi, I'm Tabish , a data scientist passionate about uncovering insights and driving decisions with data. I specialize in machine learning, data visualization, and statistical analysis to help organizations make data-driven decisions. Let's connect and explore how data can drive your business
Steps for completing your project
After purchasing the project, send requirements so Tabish can start the project.
Delivery time starts when Tabish receives requirements from you.
Tabish works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Gathering & Knowledge Base Audit
I will analyze your current support documentation to evaluate structure and content readiness. We will align on the target platform (e.g., website widget, WhatsApp, Slack) and define the agent’s specific persona, tone, and guardrails
Data Ingestion & Chunking Pipeline Strategy
I will set up the document processing pipeline. This involves cleaning your raw text, breaking documents down into optimal "chunks" (semantic pieces), and picking the right chunking strategy to ensure the AI doesn't lose context during retrieval.