AI Customer Support Automation Platform

Intelligent Multi-Channel Support System using n8n, LLMs & CRM Integration

Overview

This project is a fully automated AI-powered customer support system built using n8n workflows, designed to handle incoming customer queries across multiple channels (email, web forms, chat) and respond intelligently using Large Language Models (LLMs).

The system eliminates manual support overhead by automatically classifying, responding, and routing queries while seamlessly integrating with CRM systems (GoHighLevel) to maintain customer context and interaction history.

Real-World Problem Solved

• Delayed customer responses due to manual handling
• Lack of intelligent query classification
• High support team workload
• No centralized tracking of customer interactions
• Inconsistent support quality across channels

Automation Workflow Architecture

The system is built using modular n8n workflows that orchestrate multiple services:

• Trigger Layer: Webhooks, Gmail API, or chat integrations capture incoming queries
• Processing Layer: AI classification and intent detection using OpenAI
• Enrichment Layer: Python services fetch additional context (user history, FAQs)
• Decision Layer: Conditional routing based on query type
• Action Layer: Automated replies, escalation, or CRM updates

AI Processing & Query Handling

• Intent Classification using LLM prompts (billing, technical, general inquiries)
• Entity Extraction (order IDs, names, product references)
• Context-Aware Responses generated dynamically
• Confidence Scoring to decide automation vs escalation

If confidence is high → automated response is sent. If low → query is escalated to a human agent with full context.

Python-Based Data Enrichment

Custom Python microservices were developed to enhance AI decision-making:

• Fetch customer history from database
• Retrieve relevant FAQ answers
• Normalize and clean incoming text
• Perform similarity search for known issues

CRM Integration (GoHighLevel)

• Automatically create/update contacts
• Log all interactions as activities
• Trigger follow-up workflows
• Segment users based on query type

This ensures complete visibility of customer interactions and enables long-term relationship management.

Automation Capabilities

• Instant AI-powered email replies
• Automatic ticket categorization
• Escalation to Slack or internal teams
• Follow-up email automation
• Multi-channel support orchestration

System Flow (Example)

1. User sends support email
2. n8n webhook triggers workflow
3. LLM classifies intent
4. Python service enriches data
5. AI generates contextual response
6. Email reply is sent automatically
7. Interaction logged in GoHighLevel CRM

Impact & Results

• Reduced support response time to near real-time
• Automated handling of repetitive queries
• Improved customer satisfaction
• Scalable support system without increasing team size

This project showcases deep expertise in workflow automation, AI integration, backend services, and CRM orchestration — delivering a production-ready intelligent support system.

MYSH Unix Shell

POSIX-Style Command Line Shell Built in C

Summary

MYSH is a compact POSIX-style Unix command-line shell implemented in C. It provides a functional set of shell capabilities including command parsing, pipelines, input/output redirection, job control, and builtin commands.

The shell was designed to demonstrate core systems programming concepts such as process creation, signal handling, process groups, and file descriptor management. MYSH closely follows the behavior of traditional Unix shells, making it useful both as an educational tool and as a practical demonstration of low-level operating system concepts.

Key Features

• Command Parsing with tokenization and basic quoting support.
• Pipelines supporting multi-stage command chaining using the pipe operator |.
• Redirection support including <, >, >>, numeric file descriptor redirection (2>file) and descriptor duplication (2>&1).
• Job Control for background processes using & with job tracking and control commands.
• Built-in Commands including cd, pwd, exit, echo, alias, unalias, type, help, export, set, unset, and history.
• Execution Engine implementing fork/exec with correct process-group handling and foreground/background execution semantics.

Design & Architecture

MYSH follows a classic Unix shell architecture with modular components responsible for parsing commands, executing processes, and managing jobs.

• REPL Layer collects logical command lines and supports multi-line quoted inputs.
• Tokenizer & Parser convert raw input into a small command pipeline AST.
• Executor manages pipes, process creation, file descriptor redirection, and builtin execution.
• Job Control System tracks background jobs and safely reaps child processes using SIGCHLD.

Runtime Execution Flow

1. Read input from the user and construct a logical command line.
2. Tokenize the command while respecting quoted strings and escape sequences.
3. Build pipeline structures where each stage represents a command execution unit.
4. Create pipes, fork processes, configure redirections, and execute commands.
5. Parent process manages foreground/background execution and updates the job table.

Core Data Model

Internally, MYSH relies on structured representations for commands and job management.

• command_t — represents a pipeline stage containing arguments, redirection settings, and pipeline links.
• job_t — tracks job metadata including job ID, process group ID, command string, child PIDs, state, and exit status.

Built-in Commands

MYSH implements several built-in commands commonly found in Unix shells. Commands that modify shell state such as cd or export are executed in the parent process, while other builtins can run inside pipeline child processes when required.

Redirections are applied transparently so builtins behave consistently with external commands.

Example Commands

# basic command
echo hello

# pipeline example
/usr/bin/printf "a\nb\nc\n" | /usr/bin/grep b | /usr/bin/wc -l

# file redirection
echo redir_test > /tmp/mysh_test1

# background job
sleep 10 &
jobs
fg %1

Build & Execution

# build project
make

# start interactive shell
./bin/mysh

# run scripted commands
printf 'echo hello' | ./bin/mysh

Future Improvements

• Enhanced variable and word expansion.
• Persistent command history with line editing.
• Support for here-documents.
• Improved terminal foreground control using tcsetpgrp.

Detailed documentation, build instructions, and technical examples are provided in the project documentation.