Campaign
What are the requirements of the generative AI era, and how does MegazoneCloud AIR respond?
Today's enterprises generate millions of data points every day.
Yet the voices heard throughout the organization remain the same.
"I don't know where the data I need is."
"It takes days just to start with infrastructure requests to build an analysis environment."
"Can we use this data? Who manages it?"
This situation is not just an inconvenience for the IT department, but a fundamental obstacle preventing the entire organization from executing its strategy.
No matter how much data you have, if it doesn't translate into business results, it becomes a "burden" rather than an "asset."
Generative AI is not just a technology, but a catalyst for AI-Native transformation
The emergence of ChatGPT carries significance beyond technology. What enterprises need is not just a chatbot, but accurate and responsible knowledge delivery, and process innovation that encompasses the entire organization.
Now the important question is not "Should we adopt AI?" but "How prepared is our organization for AI?"
To strategically answer this question, Megazone Cloud has developed the AIR platform.
AIR is not just a tool, but an execution-focused framework that provides integrated support for an enterprise's AI journey from strategy formulation to implementation and scaling.
Megazone AIR Platforms
"AI begins with strategy and is completed through execution." Based on this principle, Megazone Cloud has developed the 'AIR platform' by consolidating its execution experience and technical expertise.
- AIR Studio – A space to design and operate AI Agents that actually execute business tasks
- AIR Datahub – An integrated hub supporting exploration, analysis, and visualization based on trustworthy data
- AIR AIOps – An MLOps environment that automates the entire lifecycle from AI model training to operation and resource optimization
(1) AIR Studio
Beyond chatbots, into the era of AI Agents that execute tasks – AIR Studio opens the beginning of practical AI transformation
Many enterprises ask:
"How can we actually apply generative AI to our business operations?"
Through ChatGPT, they have felt the possibilities, and from other companies' adoption cases, they have sensed the potential. However, when they try to design it to fit their own organization, they encounter problems.
- How can we connect real-world data that mixes unstructured documents, structured databases, and external information?
- Can we directly create AI agents that match our organization's unique business processes?
- Won't this just end up as another PoC?
To clearly answer these practical questions, Megazone Cloud developed AIR Studio. AIR Studio is not just a simple chatbot builder, but a practical platform that enables enterprises to connect their own data and directly design, test, and execute AI Agents capable of performing actual business tasks.
Moving beyond "conversational AI" to "task-execution AI"
The agents designed in AIR Studio are not just AI that maintains conversations, but execution-type Agents that automatically perform substantive tasks such as customer service, policy inquiries, internal knowledge responses, and operational support.
For example:
- Extract only 'HR regulations' from thousands of internal policy documents and summarize them,
- Query inventory quantities in real-time from a database,
- Collect and compare competitor prices from the web,
- Summarize the results and provide them to users – a single Agent can handle this entire series of tasks.
RAG-based structure for high-confidence responses
AIR Studio provides three types of RAG (Retrieval-Augmented Generation) tailored to various data structures:
- Document RAG: Responses based on unstructured documents such as PDFs and policy documents
- Table RAG: Information responses based on tables such as ERP, CRM, and product databases
- Web RAG: Latest information citation and summary responses based on web search
AIR Studio's agents are not just eloquent AI, but generate trustworthy responses with clear sources based on actual data.
QIQO Philosophy – "Quality In, Quality Out"
The central philosophy of AIR Studio is QIQO (Quality In, Quality Out). In other words, good data input produces good results. Without quality data input, reliable results cannot be expected. AIR Studio goes beyond simple data connection and provides a quality management framework that allows real-time testing and improvement of indexing, search settings, and response structures.
Business practitioners can improve agent quality in the following ways:
- Directly inspect the quality and composition of datasets,
- Evaluate response reliability, citation sources, and expression structures,
- Continuously enhance Agent completeness through iterative testing.
A practical environment anyone can design
AIR Studio provides an intuitive UI/UX that can be easily used by business practitioners, not just professional developers.
- Chat-based interface for Agent testing
- Prompt configuration and scenario flow setup
- Visualization of connected datasets and Agent structure
AIR Studio is not just an adoption, but the starting point for practical transformation connected to 'AI unique to our organization'.
A practical tool that makes generative AI actually 'work' within the organization – that is AIR Studio.
(2) AIR Datahub – Ensuring data-driven reliability, automating the entire process from data exploration to analysis
"The success of generative AI ultimately starts with data." AIR Datahub is structured around two axes.
- Catalog: Data exploration based on natural language and keyword search, profiling, and standardization management functions
- Portal: Self-service analysis environment, query execution, visualization tool integration (SageMaker, QuickSight, etc.)
AIR Datahub is not just a tool for 'finding data'
but a comprehensive data democratization tool that enables analysts to directly verify data, open analysis environments, and complete visualization. It realizes trustworthy data-driven AI with the following features:
- Natural language-based search + metadata-focused exploration
- Automatic detection of personal information inclusion and sensitivity levels
- Standardization, terminology management, domain definition
- Analysis sandbox, visualization tool integration (Amazon QuickSight, etc.)
(3) AIR AIOps – Agile automation of AI infrastructure and model operations
The AIOps platform is a hybrid MLOps environment designed to efficiently operate resources and pipelines between on-premises and public cloud.
- Template-based pipelines to minimize code complexity
- Automatic GPU resource recovery and usage optimization
- Unified model training, serving, performance monitoring, and version management
- Integration with open-source ML tools (Mlflow, AimStack, etc.)
AIR AIOps, which connects fragmented AI development environments into a consistent operational system, enables 'sustainable AI operations'.
Strategy leads technology
The core of AIR can be said to be that strategy comes before technology. Megazone AIR provides execution scenarios for why enterprises should adopt generative AI and how they can successfully scale it.
Megazone AIR is an AI-Native transformation tool that enables enterprises to convert data into assets, layer AI on top, and make it available to the entire organization. This journey is not a single project, but a sustainable change management process and a new standard for digital transformation. AI is no longer an experiment. It is a structural change where organizational strategy, culture, leadership, data, and security are all interconnected. AIR connects every stage of this structural change in an execution-focused manner. Megazone AIR is the starting point for turning executable AI strategies into reality.