CRM AI Agent: Autonomous Email Intelligence Platform

A production-grade Agentic CRM system that combines LLM, RAG, & autonomous workflows to understand customer conversations, make intelligent support decisions, and reduce manual customer service.

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CRM AI Agent – Autonomous Email Intelligence Platform

Inspiration

Customer support teams receive hundreds of emails every day ranging from pricing questions and technical issues to refund requests and legal escalations. Manually reading, categorizing, prioritizing, and responding to these conversations is time-consuming and often leads to delayed responses.

We wanted to build an AI-powered CRM agent capable of understanding customer conversations, retrieving relevant company knowledge, making intelligent decisions, and assisting support teams with automated actions. Our goal was to demonstrate how Agentic AI and Retrieval-Augmented Generation (RAG) can improve customer support workflows while reducing manual effort.

What it does

CRM AI Agent is an autonomous customer support intelligence platform that processes customer email threads and performs multiple AI-driven tasks:

• Email ingestion and thread management • Thread-level classification • Sentiment analysis • Urgency detection • Human escalation detection • Knowledge retrieval using RAG • Agent decision making • AI-generated support replies

The platform automatically analyzes entire conversation threads instead of isolated emails, allowing it to make more informed support decisions.

Examples include:

• Escalating legal complaints • Flagging critical outages • Detecting customer churn risks • Handling refund requests • Responding to pricing inquiries • Identifying spam messages

How we built it

Backend

The backend was built using FastAPI and SQLAlchemy.

Customer emails are loaded from a simulated enterprise email dataset and stored in a SQLite database containing:

• Emails Table • Threads Table • Contacts Table

The backend exposes REST APIs for:

• Email retrieval • Thread management • Classification • Agent reasoning • Knowledge retrieval • Reply generation

Knowledge Base and RAG

A Retrieval-Augmented Generation pipeline was implemented using ChromaDB.

Six business knowledge documents are indexed:

• Pricing Policy • Refund Policy • SLA Policy • API Documentation • Compliance FAQ • Escalation Matrix

When the AI agent receives a customer query, it retrieves the most relevant business context before generating a decision or response.

Agent Decision Engine

The system contains an autonomous decision engine that determines:

• Auto Reply • Human Escalation • Legal Escalation • Technical Escalation

Decisions are based on:

• Customer intent • Urgency level • Sentiment • Escalation rules

AI Reply Generation

Google Gemini is used to generate intelligent support responses.

The model receives:

• Customer conversation • Classification results • Relevant knowledge base context

and produces context-aware replies.

Frontend

The frontend was developed using React and Vite.

The dashboard provides:

• Email thread list • Conversation viewer • Classification results • Agent decisions • Generated responses

This creates a CRM-style interface for visualizing AI reasoning and support workflows.

Challenges we ran into

Some of the main challenges included:

• Designing accurate thread-level classification logic • Managing relationships between emails and conversation threads • Building a RAG pipeline with structured knowledge documents • Integrating Gemini response generation • Connecting FastAPI APIs with React frontend components • Handling escalation scenarios consistently across different email types

We also spent significant time debugging frontend-backend communication and API integration issues.

What we learned

Throughout this project we gained practical experience with:

• Agentic AI systems • Retrieval-Augmented Generation (RAG) • FastAPI development • React frontend integration • SQLAlchemy ORM • Vector databases • ChromaDB indexing • Prompt engineering • Gemini API integration • CRM workflow automation

We also learned how intelligent agents can combine business rules, knowledge retrieval, and LLM reasoning to automate complex customer support processes.

What's next

Future improvements include:

• Real email provider integrations • Authentication and role-based access control • Advanced analytics dashboard • Multi-agent workflows • Human feedback learning loops • PostgreSQL deployment • Cloud hosting • Ticket assignment automation • Real-time monitoring and reporting

Our vision is to evolve CRM AI Agent into a production-ready autonomous customer support platform capable of handling enterprise-scale communication workflows.

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