Back to Catalog

Build a Smart Invoice Dispute Bot with CrewAI & Watsonx

BeginnerGuided Project

Build multi-agent AI systems with CrewAI and IBM Watsonx for automated billing dispute resolution. Apply in-demand AI orchestration skills, agent coordination, and LLM integration. Create intelligent agents that detect invoice anomalies, analyze billing discrepancies, and generate professional dispute responses. This hands-on project teaches essential multi-agent AI development techniques for finance automation—critical skills for AI engineers and developers building enterprise solutions in just 30 minutes!

Language

  • English

Topic

  • Data Science

Skills You Will Learn

  • watsonx, LLM, CrewAI, Python, Generative AI, Data Science

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 40 minutes

Platform

  • SkillsNetwork

Last Update

  • July 31, 2025
About this Guided Project
CrewAI is a powerful framework for orchestrating AI agents that collaborate on complex, multi-step workflows. In this hands-on project, you'll integrate CrewAI with IBM Watsonx using the LLaMA 3 model to build a smart invoice dispute resolution system. The agents will handle tasks such as parsing invoices, detecting discrepancies, validating policies, and drafting professional dispute letters—automating a process that is traditionally manual and time-consuming.

Each agent is assigned a focused role—like invoice parsing, validation, or response generation—while CrewAI handles coordination, task delegation, and shared memory. Watsonx enhances each agent’s language and reasoning capabilities, enabling a scalable, intelligent system applicable across finance, procurement, and customer service domains.

By completing this project, you’ll gain hands-on experience with multi-agent systems, schema-based prompting, and structured output generation—skills essential for building real-world AI solutions.

Who Should Enroll

  • Developers, data analysts, and business professionals looking to automate invoice-related workflows
  • Anyone eager to learn how AI agents collaborate to extract, analyze, and act on structured data
  • Learners interested in practical applications of CrewAI, IBM Watsonx, and LLM-based orchestration

Why Enroll

In just 30 minutes, you'll go from concept to execution—building an AI-powered invoice dispute bot through clear, practical steps. This project offers a real-world use case to deepen your understanding of agent collaboration, language models, and intelligent automation. Whether you're new to AI or looking to advance your skills, you'll walk away with a functional prototype and the confidence to apply these techniques in production settings.

A Look at the Project Ahead

After completing this guided project, you will be able to do:
  • Understand CrewAI's architecture – Explain the roles of agents, tasks, and the Crew structure for orchestrating multi-agent workflows.
  • Set up and integrate Watsonx LLM (LLaMA 3)- Configure and use IBM Watsonx with the LLaMA 3 model to enable structured outputs and reasoning-driven tasks within CrewAI agents.
  • Design intelligent agents – Create agents with defined roles, tools, memory, and behavior tailored to real-world scenarios like invoice processing and validation.
  • Build collaborative task workflows – Orchestrate multiple agents to perform complex, end-to-end tasks such as data extraction, validation, anomaly detection, and communication.

What You'll Need

Technical Requirements: A basic understanding of Python programming. Experience with AI or LLMs is helpful but not required.
Browser Setup: A modern web browser to access CrewAI tools, follow the guided environment, and run your code.

Instructors

Jigisha Barbhaya

Data Scientist

I am a Data scientist at IBM and Lead instructor at Skills network. I love to learn and educate. I have completed my MSc(Computer Application) specialisation in Data science from Symbiosis University.

Read more

Contributors

Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

Read more