Custom GPTs & Internal AI Assistants

Build purpose-specific AI tools tailored to your organization.

Pillar

Technology – Platforms, Tools, Infrastructure & Productivity

Overview

This course teaches how to design, develop, and deploy custom Generative Pre-trained Transformers (GPTs) and AI assistants specifically tailored to meet unique organizational needs. Participants will explore the entire lifecycle of building internal AI tools—from understanding business requirements and data integration to creating conversational AI models that improve productivity, automate workflows, and enhance employee and customer experiences.

Learning Objectives

Participants will be able to:

• Understand the fundamentals of custom GPTs and AI assistants

• Identify organizational use cases for AI-powered tools

• Develop and fine-tune GPT models to specific business contexts

• Integrate internal data securely for personalized AI responses

• Deploy AI assistants within enterprise systems and monitor performance

Target Audience

• AI developers and data scientists

• IT architects and solution engineers

• Business analysts and product managers

• Innovation teams focusing on AI adoption

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Hands-on workshops with GPT customization platforms

• Case studies on successful internal AI assistant deployments

• Guided labs on data integration and model fine-tuning

• Group discussions on governance and ethical AI deployment

Materials Provided

• Step-by-step guides for building custom GPTs

• Sample datasets and integration templates

• Access to AI development sandbox environments

• Frameworks for responsible AI deployment

Outcomes

• Ability to create tailored GPT models aligned with business goals

• Skills to securely integrate enterprise data for AI training

• Competence in deploying and managing AI assistants internally

• Knowledge of best practices in AI ethics and governance

Outline / Content

Day 1: Introduction to Custom GPTs and Use Case Identification

• Overview of GPT technology and customization possibilities

• Mapping organizational needs to AI tool design

Day 2: Data Integration and Model Fine-Tuning

• Preparing and integrating internal data for AI training

• Techniques for fine-tuning GPTs to domain-specific language

Day 3: Deployment and Integration

• Embedding AI assistants in enterprise workflows and apps

• Setting up monitoring, feedback, and continuous improvement

Day 4: Governance, Security, and Future Trends

• Ethical considerations and compliance for internal AI tools

• Case studies of AI assistant success stories

• Emerging trends in custom AI assistant technologies