Platform Overview and Core Concepts

Architecture summary, core modules, and key concepts of the Qubitz AI system.

Platform Overview

Architecture Summary

Qubitz AI is a cloud-native Agentic AI system built exclusively on Amazon Web Services (AWS). It provides an end-to-end workflow for enterprise AI adoption -- from initial business context analysis through automated use case discovery, multi-agent deep research, and production-grade deliverable generation.

The platform's core engine is a multi-agent orchestration system that coordinates 7 specialized AI agents across 6 execution phases. These agents perform web research, market analysis, structured report writing, and use case prioritization autonomously, producing three comprehensive deliverables in a single 8-minute pipeline run.

Beyond the initial analysis, Qubitz provides a post-analysis workspace with team collaboration features, on-demand artifact generation (presentations and architecture diagrams), an integrated AI chat assistant for document Q&A, and a Design App for prototyping AI applications from generated use cases.

Core Modules

The platform is organized into three primary modules accessible from the Project Selection hub:

My Projects

The project management workspace. Stores all completed analyses with their generated reports, use case cards, collaboration features (ratings, comments), and artifact generation capabilities. Each project is identified by an auto-generated ID derived from the company name and a numeric suffix (e.g., cloud202-897).

Use Case Tool

The AI-powered discovery engine. Accepts business context via a structured form plus optional document uploads, then executes a multi-agent research pipeline to produce the AI Discovery Report, Deep Research Report, and Use Case List. This is the platform's flagship feature and the primary entry point for new users.

Use Case Catalogs

A library of pre-built industry-specific use case templates. Currently in development (marked "Coming Soon"). When available, users will be able to browse and apply template use cases directly to their projects without running a full Deep Analysis.