AI Agent Portfolio for Business Automation

AI Agent Portfolio for Business Automation Welcome to my portfolio of AI agent demos. This repository showcases my ability to design, build, and deploy intelligent agents using LangChain and Large Language Models (LLMs) to solve real-world business challenges through automation.

Each agent is a self-contained project designed to demonstrate how AI can drive efficiency, provide actionable insights, and automate complex workflows in different industries.

AI Agent Demos This repository contains the following five demo agents:

AI Lead Qualification Bot Purpose: Automates the initial screening of sales leads.

Problem it Solves: Reduces the time sales teams spend on low-potential leads by quickly assessing a lead's fit against predefined criteria.

Key Features: Uses LLMs to intelligently score leads, configurable qualification criteria, and provides a clear rationale for each classification.

AI-Powered Marketing Campaign Performance Summarizer Purpose: Transforms raw marketing campaign data into executive-ready reports.

Problem it Solves: Eliminates the manual, time-consuming process of compiling performance reports, giving marketing agencies instant, actionable insights.

Key Features: Analyzes mock campaign data, highlights key KPIs (like CPC, ROI), identifies trends, and suggests potential next steps.

AI-Powered Social Media Content Generator for Agencies Purpose: Automates the creation of creative and platform-specific social media content.

Problem it Solves: Drastically boosts content output and ensures brand consistency for marketing agencies, freeing up creative teams for strategic work.

Key Features: Generates multiple post options tailored for platforms like LinkedIn, Instagram, and X (Twitter), complete with hashtags and emojis where needed.

Automated Healthcare Regulatory Compliance Digest Agent Purpose: Intelligently tracks and summarizes complex regulatory updates for healthcare organizations.

Problem it Solves: Mitigates risk and reduces the administrative burden of staying compliant with ever-changing regulations (e.g., from the NHS or CMS).

Key Features: Extracts key changes from mock policy documents, analyzes their operational impact on different departments (IT, Finance, HR), and provides a concise digest.

Disclaimer: This is a DEMO ONLY. It uses mock data and is not intended for clinical use or handling PHI.

Clinical Trial Participant Pre-Qualification Agent Purpose: Assists healthcare research administrators by pre-qualifying simulated patient profiles for clinical trials.

Problem it Solves: Streamlines the labor-intensive administrative task of patient screening, helping to accelerate medical research.

Key Features: Compares mock patient profiles against trial eligibility criteria and provides a clear pre-qualification status with a specific rationale.

Disclaimer: This is a DEMO ONLY. It uses mock, de-identified data and is not intended for clinical advice or handling PHI.

Technology Stack Framework: LangChain

Language: Python

LLM: OpenAI (via API)

Dependencies: langchain, langchain-community, langchain-openai, openai, python-dotenv, pandas

How to Use Each agent is located in its own sub-directory with a dedicated README.md file that contains specific setup instructions, usage examples, and details on how it works.

To run any of the demos:

Clone this repository: git clone [your-repo-url]

Navigate to the specific agent's folder: cd [agent-folder-name]

Follow the instructions in that folder's README.md to set up the environment and run the bot.

Feel free to explore the code, experiment with the demos, and reach out if you have any questions.