Why AWS Generative AI Solution Box?
Build generative AI applications easily, even without development experience
Fast : Build various generative AI solutions with one click
Easy-to-use : Carefully selected solutions that even beginners can immediately experience the benefits
Secure : Production-ready security for immediate production use
Open-Source : Each solution is open source and customizable
Guide : Provides usage instructions and guides for adoption
3-Step Build Process
1
Login AWS
Create an AWS Account and
login with deployment user
login with deployment user
2
Choose & Click
Select the solution you want
Click to start deployment
Click to start deployment
3
Start Journey
Start using when completion
notification arrives
notification arrives
1. Prepare AWS Account
Please create an AWS account and sign in by referring to "Point 2: How to start using AWS?" in 6 Points for AWS Beginners.
2. Choose & Click
Once you've decided on the AWS solution you want to use, select a region and click Deploy. If you need guides such as explanations of deployment options, please refer to the detailed documentation.
Find by Challenge
Select Solutions by Industry Challenges
Document/Form Reading (OCR)
- Large volume of documents to read, including financial reports
- Manual entry is time-consuming and preventing human errors is a challenge
- Becomes a bottleneck for expediting reviews and decisions based on input data
💡 Document reading with generative AI : GenU: Document reading with various models
Document Review & Assessment
- Enormous volume of documents to review including applications, contracts, and financial reports
- Review items span many areas, with periodic revisions due to regulatory and policy changes
- Work concentrates on specific experts, making review time a bottleneck
💡 Document review support with generative AI : RAPID: Document review solution
Call Center
- Wide range of financial products and insurance services to handle
- Need to understand plan details, coverage amounts, and new/discontinued plans
- Significant training time required for new operators to become proficient
💡 Sales training with generative AI : AI Sales Roleplay
💡 Response support with generative AI : GenU: Document-based response support
💡 Response support with generative AI : GenU: Document-based response support
Requirements Review & Estimation
- Requirement specifications for product development and projects can span hundreds of pages
- Accurate understanding of specifications is essential for accurate estimates, with no room for oversight
- Cross-referencing with past cases is indispensable, but documents are often scattered
💡 Document review support with generative AI : RAPID: Document review solution
💡 Knowledge base construction with generative AI : GenU: Knowledge base construction
💡 Knowledge base construction with generative AI : GenU: Knowledge base construction
Data Utilization in Manufacturing Processes
- IoT adoption generates diverse data, but the volume is enormous
- Shortage of personnel who can interpret the meaning of data
- Beyond dashboards, obtaining data-driven answers based on context is essential
💡 Analytics workflow with generative AI : Dify: Build AI workflows through GUI
Quality Inspection Optimization
- Visual inspection remains a critical process in quality inspection
- Large machinery and vehicles are sizable with extensive inspection items
- Labor shortages lead to resource constraints and missed defects
💡 Image recognition with generative AI : GenU: Image & video analysis
Maintenance & After-Sales Service
- Wide variety of product lineups including machinery and precision instruments
- Difficult to track specifications, required parts, and equipment for each product
- Efficiency issues such as missing necessary parts during on-site repairs
💡 Response support with generative AI : GenU: Document-based response support
Marketing Content Creation
- Personalization is important but difficult to customize content for each individual member
- Want to send timely communications about new products, but limited writers constrain frequency
- Template-based efficiency risks creating déjà vu and reducing customer loyalty
💡 Writing support with generative AI : GenU: Document creation
Product Creative Production
- Growing demand for product images with the spread of e-commerce
- Limited photo opportunities for seasonal and expensive products, with scheduling challenges
- Different image size and specification requirements across e-commerce platforms
💡 Apparel image generation with generative AI : GenAI Design Studio: Virtual try-on
💡 Image & description generation with generative AI : GenU: Image generation & description creation
💡 Image & description generation with generative AI : GenU: Image generation & description creation
Customer Service
- In-store experience remains a crucial customer touchpoint even with e-commerce growth
- Labor shortages make adequate service and training difficult
- Addressing customer harassment, which has become a social issue, is essential
💡 Customer service with generative AI : BrChat: From internal validation to external API deployment
💡 Sales training with generative AI : AI Sales Roleplay
💡 Sales training with generative AI : AI Sales Roleplay
Building Healthy Customer Communities
- Customer reviews and communities are important parts of the product experience
- Malicious or offensive posts from trolls pose a significant risk to communities
- The volume of reviews and comments is enormous, making thorough checks difficult
💡 Comment detection with generative AI : BrChat: From internal validation to integration with internal systems via API
Public Document Creation
- Public documents come in various formats, each requiring specific formatting
- Time spent on document creation reduces time for engaging with residents and patients, increasing overtime
- Variations in content across different authors make data utilization difficult
💡 Document creation support with generative AI : GenU: Generative AI platform deployable even in closed networks
Document Scanning
- Paper-based data still persists in public institutions and healthcare settings
- Significant effort required to read and digitize handwritten information
- Valuable human resources are allocated to mechanical tasks amid growing labor shortages
💡 Document scanning with generative AI : GenU: Image reading with various models
Accelerating Development with Generative AI
- Direct code generation by AI faces quality challenges
- Lack of documentation to support generated code
- Significant skill gaps in how people provide instructions
💡 Step-by-step support for development starting from "specification definition" : Kiro IDE: Spec-driven development IDE
Development Environment
- AI agent development requires installing various frameworks and configuring deployments
- Frequent installation errors and configuration issues
- Environment differences and setup effort become bottlenecks for democratizing AI agent development
💡 Cloud-based development environment : AI Agent Development Code Server: Cloud-based AI agent development environment
Development Quality Management
- Need to identify security and performance concerns early in product development
- Limited review time and risk of oversights in unfamiliar areas
- Large features/fixes lead to wide-ranging feedback requiring multiple review cycles
💡 Develop in collaboration with custom agents : Bedrock Engineer: A development agent for "you"
💡 Automate reviews and fixes : Remote SWE Agents: Autonomous software development agents
💡 Automate reviews and fixes : Remote SWE Agents: Autonomous software development agents
Find from List
Generative AI Use Cases is an application with various generative AI use cases pre-built. It's ideal for building a safe and easy-to-use environment for everyone when promoting the adoption of generative AI within your organization.
Initial deployment: Use the Deploy button.
Updates after deployment: Use the Update button to inherit previous settings by entering only Environment and NotificationEmailAddress (leave others as default values). (Check detailed method)
Updates after deployment: Use the Update button to inherit previous settings by entering only Environment and NotificationEmailAddress (leave others as default values). (Check detailed method)
Dify allows you to create chatbots and workflows using generative AI through a GUI. It's ideal when you want to implement multi-step generative AI processing. For AWS deployment, you can easily deploy using dify-self-hosted-on-aws.
Kiro is a "specification-driven" development environment that supports the entire development process from specification creation to design and coding. Kiro IDE Remote allows you to access a development environment with Kiro, Kiro CLI, AWS CLI, and other tools pre-installed directly from your browser.
Cursor is an AI-powered code editor that significantly boosts developer productivity. Cursor IDE Remote allows you to access a development environment with Cursor, AWS CLI, and other tools pre-installed directly from your browser. It provides a secure environment with AppArmor on Ubuntu 24.04.
AI Agent Development Code Server is a dedicated development environment for AI agent development using Amazon Bedrock Agent Core. It provides a browser-based VS Code (code-server) development environment that runs entirely on AWS.
RAPID is a document review solution powered by generative AI (Amazon Bedrock). It streamlines review processes involving extensive documents and complex checklists using a Human in the Loop approach.
AI Sales Roleplay is a roleplaying system for improving sales skills using generative AI. Through voice conversations with emotionally expressive AI, you can develop practical sales skills.
Bedrock Chat is a multilingual generative AI platform powered by Amazon Bedrock. It supports not only simple chat functionality but also custom bot creation using knowledge bases (RAG), bot sharing through a bot store, and task automation using agent functionality.
GenAI Design Studio is a virtual try-on solution powered by Amazon Nova Canvas. It aims to streamline various processes in the apparel industry and e-commerce services, from fashion design to actual model photography.
Customer 360 Data Fusion leverages AWS Entity Resolution to match and integrate customer data across different data sources, enabling natural language segment creation for comprehensive Customer 360 implementation.
Bedrock Engineer is an autonomous software development agent application powered by Amazon Bedrock. You can customize and use various features such as file creation/editing, command execution, web search, knowledge base utilization, multi-agent collaboration, and image generation.
Remote SWE Agents is an example implementation of a fully autonomous software development AI agent. This agent operates in a dedicated development environment for each task, performing development work without depending on the user's PC.
3. Start Journey
For Generative AI Use Cases, you can learn how to use it by following the next workshop.