AI Product Workspace Platform

1. Project Title
AI Product Workspace Platform

2. Executive Summary
The AI Product Workspace Platform is a web-based application designed to centralize product development workflows using AI-powered agents tailored to different roles within a team.
The platform enables Business Analysts, Product Owners, Designers, Developers, and QA engineers to collaborate within a single project environment, generating artifacts such as user stories, wireframes, code, and test cases using AI.
It integrates with tools like Jira and will evolve into a marketplace for AI agents and extensions.

3. Objectives
* Centralize product development workflows
* Reduce dependency on multiple disconnected tools
* Accelerate delivery using AI-generated outputs
* Provide role-specific AI assistance
* Enable integration with existing tools (starting with Jira)

4. Scope
4.1 In Scope (Phase 1 - MVP)
* User authentication
* Project creation and management
* Single chat interface per project
* Role-based AI agents:
* Business Analyst Agent
* Designer Agent
* Developer Agent
* Shared project context (memory)
* Structured outputs:
* User stories
* Wireframes (text-based or simple representation)
* Code snippets
* Basic credit/usage system

4.2 Out of Scope (Phase 1)
* Marketplace
* QA/Test agent
* Real-time collaboration
* Advanced automation
* Full Jira synchronization
* Enterprise security features

5. Target Users
* Business Analysts (BA)
* Product Owners (PO)
* UI/UX Designers
* Developers
* QA/Testers (future phase)

6. User Roles & Capabilities
6.1 Business Analyst / Product Owner
* Input requirements
* Generate user stories
* Define acceptance criteria

6.2 Designer
* Generate wireframes
* Suggest UI/UX improvements

6.3 Developer
* Generate code snippets
* Suggest architecture
* Convert requirements into implementation

7. Core Features
7.1 Authentication
* Email/password login
* Basic session management

7.2 Project Management
* Create project
* View project list
* Open project workspace

7.3 Chat System
* Single chat per project
* Message history storage
* AI-generated responses

7.4 AI Agent System
Agents are role-based but share the same project context.
Agents:
* BA Agent
* Designer Agent
* Developer Agent
Capabilities:
* Generate outputs based on prompts
* Access shared project data

7.5 Context Memory System
The system stores and organizes outputs:
* User Stories
* Design Outputs
* Code Snippets
Agents can reference previous outputs.

7.6 Output Structuring
Each generated output is categorized:
* Story
* Design
* Code
Users can:
* View outputs separately
* Reuse them in future prompts

7.7 Credit System
* Free tier with limited usage
* Usage tracking per user
* Restrict access when credits are exhausted

8. User Flow
Step 1:
User signs up / logs in
Step 2:
User creates a project
Step 3:
User enters project workspace
Step 4:
User interacts with AI agents via chat
Step 5:
System generates:
* User stories
* Designs
* Code
Step 6:
Outputs are stored and reused

9. Technical Requirements
9.1 Frontend
* Framework: React (Next.js)
* Responsive design
* Chat interface

9.2 Backend
* Node.js (Express) or Python (FastAPI)
* REST API

9.3 AI Integration
* Integration with OpenAI API or equivalent

9.4 Database
* PostgreSQL

9.5 Hosting
* Cloud-based (AWS, Vercel, or similar)

10. Non-Functional Requirements
* Performance: <2s response time (excluding AI latency)
* Scalability: Support multiple concurrent users
* Security: Basic authentication and data protection
* Availability: 99% uptime target

11. Deliverables (From Development Company)
* Fully functional web application (MVP)
* Source code (frontend + backend)
* Database schema
* API documentation
* Deployment setup
* Basic testing

12. Timeline
Estimated Duration: 4–6 weeks (MVP)

13. Future Phases (Post-MVP)
* Integration with Jira
* Multi-user collaboration
* QA/Test agent
* Plugin system
* Marketplace
* Advanced automation

14. Success Criteria
* Users can complete:
* Idea → User Story → Design → Code
* Stable system with minimal errors
* Positive feedback from initial users

15. Budget Considerations
(To be defined with development company)
Include:
* Development cost
* AI API usage cost
* Hosting cost

Optional Section (Strongly Recommended)
16. Questions for Development Company
Ask them:
* What architecture do you recommend?
* How will you handle AI cost optimization?
* How will scalability be managed?
* What is your testing approach?
* What happens after delivery (support/maintenance)?