# ColaFlow Product Requirements Document (PRD)

**Version:** 1.0
**Date:** 2025-11-02
**Product Manager:** ColaFlow PM Team
**Status:** Draft

---

## Executive Summary

ColaFlow is a next-generation AI-powered project management system built on the Model Context Protocol (MCP). It aims to revolutionize team collaboration by making AI a first-class team member that can safely read, write, and manage project data while maintaining human oversight and control.

### Vision Statement
> "Flow your work, with AI in every loop."

### Mission
To create a platform where human-AI collaboration flows naturally, enabling AI to automatically generate and update tasks, documents, and progress reports while humans maintain decision-making authority through secure review and approval mechanisms.

### Strategic Goals
1. **AI-Native Project Management**: Enable AI tools to directly participate in project workflows
2. **Seamless Integration**: Connect ChatGPT, Claude, GitHub, Calendar, Slack, and other tools through MCP
3. **Secure Collaboration**: Provide auditable, safe, and reversible AI operations
4. **Agile Compatibility**: Support Jira-style agile methodologies (Epic/Story/Task/Sprint/Workflow)
5. **Platform Hub**: Become the central hub for development and collaboration

---

## Product Overview

### What is ColaFlow?

ColaFlow is an intelligent project management platform inspired by Jira's agile management model but enhanced with AI capabilities and open protocol integration. It enables:

- **AI-Human Collaboration**: AI can propose changes, generate content, and automate workflows with human approval
- **MCP Protocol Integration**: Universal connectivity with AI tools and external systems
- **Full Project Lifecycle**: From idea to delivery with AI assistance at every stage
- **Security & Auditability**: All AI operations are logged, reviewable, and reversible

### Target Users

**Primary Users:**
- Product Managers: Project planning, requirement management, progress tracking
- Software Developers: Task execution, code integration, technical documentation
- Team Leads: Resource allocation, sprint planning, team coordination
- AI Power Users: Advanced automation, workflow optimization

**Secondary Users:**
- QA Engineers: Test planning, bug tracking, quality metrics
- Stakeholders: Progress visibility, report generation, decision support
- External AI Agents: Automated task creation, documentation, reporting

### Value Proposition

**For Teams:**
- **30% faster project creation** through AI-assisted planning
- **50%+ automated task generation** reducing manual overhead
- **Unified workflow hub** eliminating tool fragmentation
- **Complete audit trail** ensuring accountability and compliance

**For Organizations:**
- **Reduced administrative burden** on project managers
- **Improved visibility** into project health and risks
- **Scalable processes** that grow with team size
- **Future-proof platform** built on open standards (MCP)

---

## Core Requirements

### Functional Requirements

#### 1. Project Management Core (M1 Priority)

**1.1 Project Hierarchy**
- Support Epic → Story → Task → Sub-task structure
- Customizable workflows and statuses (To Do → In Progress → Review → Done)
- Project templates and cloning capabilities
- Cross-project dependencies and linking

**1.2 Task Management**
- Rich task attributes: priority, assignee, labels, due dates, estimates
- Custom fields and metadata
- Attachment support (documents, images, links)
- Comment threads and @mentions
- Task history and activity log

**1.3 Visualization**
- Kanban boards with drag-and-drop
- Gantt charts for timeline planning
- Calendar view for scheduling
- Burndown charts for sprint tracking
- Custom dashboards and filters

**1.4 Audit & Version Control**
- Complete change history for all entities
- Rollback capability with transaction tokens
- Field-level change tracking
- User action attribution

#### 2. MCP Integration Layer (M2 Priority)

**2.1 MCP Server**

**Resources Exposed:**
- `projects.search` - Query projects by various criteria
- `issues.search` - Search tasks across projects
- `docs.create_draft` - Generate document drafts
- `reports.daily` - Access daily progress reports
- `sprints.current` - Current sprint information
- `backlogs.view` - Product backlog access

**Tools Exposed:**
- `create_issue` - Create new tasks/stories/epics
- `update_status` - Modify task states
- `assign_task` - Assign resources
- `log_decision` - Record key decisions
- `generate_report` - Create progress summaries
- `estimate_task` - Add time estimates

**Write Operation Flow:**
```
AI Request → Generate Diff Preview → Human Review → Approve/Reject → Commit/Discard
```

**2.2 MCP Client**

**External System Connections:**
- **GitHub**: PR status → Task updates, commit linking
- **Slack**: Notifications, daily standups, AI summaries
- **Calendar**: Sprint events, milestones, deadlines
- **Future**: Jira import, Notion sync, Linear integration

**Event-Driven Automation:**
- PR merged → Auto-update task status to "Done"
- Sprint started → Send team notifications
- Risk detected → Alert stakeholders
- Document changed → Notify subscribers

**2.3 Security & Compliance**

**Authentication:**
- OAuth 2.0 for external integrations
- API token management for AI agents
- Session management and timeout policies

**Authorization:**
- Role-based access control (RBAC)
- Field-level permissions
- Operation whitelisting for AI agents
- Read vs. Write permission separation

**Audit:**
- All operations logged with timestamp, user, and action
- Diff storage for rollback capability
- Compliance reporting (GDPR, SOC2)
- Retention policies for audit logs

#### 3. AI Collaboration Layer (M3 Priority)

**3.1 Natural Language Interface**
- Create tasks from conversational descriptions
- Generate documentation from requirements
- Parse meeting notes into action items
- Query project status in plain language

**3.2 AI-Powered Features**

**Automated Generation:**
- Task breakdowns from high-level descriptions
- Acceptance criteria suggestions
- Time estimates based on historical data
- Risk assessments for delayed tasks
- Daily standup summaries
- Weekly progress reports

**Intelligent Suggestions:**
- Missing information detection (e.g., no acceptance criteria)
- Priority recommendations based on deadlines
- Resource allocation optimization
- Sprint capacity planning

**3.3 AI Control Console**

**Features:**
- Visual diff display for AI-proposed changes
- Side-by-side comparison (current vs. proposed)
- Batch approval for multiple changes
- Rejection with feedback mechanism
- AI operation history and statistics

**User Experience:**
- Clear indication of AI-generated content
- Confidence scores for AI suggestions
- Explanation of AI reasoning
- Easy approve/reject/modify workflow

**3.4 Prompt Template Library**

**Template Categories:**
- Requirements analysis templates
- Acceptance criteria generation
- Task estimation prompts
- Risk identification frameworks
- Report generation formats
- Code review summaries

**Customization:**
- Organization-specific templates
- Project-level overrides
- Variable substitution
- Version control for templates

**3.5 Multi-Model Support**
- Claude (Anthropic)
- ChatGPT (OpenAI)
- Gemini (Google)
- Model switching per operation
- Cost and performance tracking

---

### Non-Functional Requirements

#### Performance
- **Response Time**: API calls < 200ms (p95), < 500ms (p99)
- **Throughput**: Support 1000+ concurrent users
- **Scalability**: Horizontal scaling for stateless services
- **Database**: Optimized queries, proper indexing, connection pooling

#### Reliability
- **Availability**: 99.9% uptime SLA
- **Data Durability**: No data loss, automated backups
- **Error Handling**: Graceful degradation, retry mechanisms
- **Monitoring**: Real-time alerts, health checks

#### Security
- **Encryption**: HTTPS for transport, AES-256 for data at rest
- **Authentication**: Multi-factor authentication (MFA) support
- **Compliance**: GDPR, SOC2, ISO 27001 ready
- **Private Deployment**: On-premise installation option

#### Usability
- **Intuitive UI**: Minimal learning curve, familiar patterns
- **Accessibility**: WCAG 2.1 AA compliance
- **Responsive Design**: Mobile, tablet, desktop support
- **Documentation**: Comprehensive user guides, API docs, video tutorials

#### Compatibility
- **Browsers**: Chrome, Firefox, Safari, Edge (latest 2 versions)
- **API**: RESTful, GraphQL, MCP protocol support
- **Integrations**: OAuth 2.0, Webhooks, SSO (SAML, OIDC)

---

## User Stories & Acceptance Criteria

### Epic 1: Core Project Management

#### Story 1.1: As a PM, I want to create projects with hierarchical tasks
**Acceptance Criteria:**
- Can create Epic → Story → Task → Sub-task hierarchy
- Each level has distinct attributes and behaviors
- Can reorder and reorganize hierarchy via drag-and-drop
- Changes are reflected immediately in all views

#### Story 1.2: As a team member, I want to visualize work in multiple formats
**Acceptance Criteria:**
- Kanban board displays tasks by status columns
- Gantt chart shows timeline with dependencies
- Calendar view shows tasks by due date
- Burndown chart tracks sprint progress
- Can switch between views seamlessly

#### Story 1.3: As a PM, I need audit trails for accountability
**Acceptance Criteria:**
- All changes are logged with user, timestamp, and changes
- Can view complete history for any entity
- Can rollback to previous state with one click
- Audit log is searchable and filterable

### Epic 2: MCP Server Integration

#### Story 2.1: As an AI tool, I want to read project data via MCP
**Acceptance Criteria:**
- MCP server exposes documented resources
- Can query projects, issues, documents, reports
- Responses follow MCP protocol specification
- Authentication is required and validated

#### Story 2.2: As an AI tool, I want to propose changes via MCP
**Acceptance Criteria:**
- Can call tools to create/update tasks
- System generates diff preview for all changes
- Changes are not committed until human approval
- Rejected changes are logged with reason

#### Story 2.3: As a user, I want to review AI-proposed changes
**Acceptance Criteria:**
- AI console shows pending changes with diffs
- Can approve, reject, or modify each change
- Batch operations for multiple changes
- Notifications for new AI proposals

### Epic 3: AI Collaboration Features

#### Story 3.1: As a PM, I want AI to generate task breakdowns
**Acceptance Criteria:**
- Can input high-level description in natural language
- AI proposes Epic/Story/Task structure
- Each task includes title, description, estimates
- Can edit and approve before committing

#### Story 3.2: As a team lead, I want automated standup reports
**Acceptance Criteria:**
- AI generates daily summary of team progress
- Includes completed tasks, in-progress work, blockers
- Posted to Slack or email automatically
- Customizable format and schedule

#### Story 3.3: As a developer, I want AI-suggested acceptance criteria
**Acceptance Criteria:**
- AI detects tasks without acceptance criteria
- Proposes criteria based on task description
- Can accept, reject, or modify suggestions
- Learns from feedback over time

---

## Success Metrics

### Primary KPIs

| Metric | Baseline | Target | Timeline |
|--------|----------|--------|----------|
| Project creation time | Current process | ↓ 30% | M3 |
| AI-automated task ratio | 0% | ≥ 50% | M4 |
| Human approval rate | N/A | ≥ 90% | M3 |
| Rollback rate | N/A | ≤ 5% | M3 |
| User satisfaction score | N/A | ≥ 85% | M5 |

### Secondary Metrics

| Metric | Target | Purpose |
|--------|--------|---------|
| API response time | < 200ms (p95) | Performance |
| System uptime | ≥ 99.9% | Reliability |
| Integration success rate | ≥ 95% | Compatibility |
| Daily active users | Track growth | Adoption |
| AI operation cost | Monitor & optimize | Efficiency |

### Business Metrics

| Metric | Target | Timeline |
|--------|--------|----------|
| Internal team adoption | 100% | M5 |
| External pilot users | 10 organizations | M5 |
| MCP tool integrations | ≥ 5 tools | M6 |
| Documentation completeness | 100% coverage | M6 |
| Community contributions | Active GitHub repo | M6 |

---

## Technical Architecture

### System Components

```
┌─────────────────────────────────────┐
│         Presentation Layer          │
│  - React Frontend (Kanban/Gantt)    │
│  - AI Control Console               │
│  - Mobile Responsive UI             │
└──────────────┬──────────────────────┘
               │ HTTPS/WebSocket
┌──────────────┴──────────────────────┐
│       Application Layer (NestJS)    │
│  - REST API                         │
│  - GraphQL API                      │
│  - MCP Server                       │
│  - MCP Client                       │
│  - WebSocket Server                 │
└──────────────┬──────────────────────┘
               │
┌──────────────┴──────────────────────┐
│        Business Logic Layer         │
│  - Project Management Service       │
│  - Task Workflow Engine             │
│  - AI Integration Service           │
│  - Permission & Auth Service        │
│  - Notification Service             │
└──────────────┬──────────────────────┘
               │
┌──────────────┴──────────────────────┐
│         Data Layer                  │
│  - PostgreSQL (Primary DB)          │
│  - pgvector (AI embeddings)         │
│  - Redis (Cache & Sessions)         │
│  - S3 (File storage)                │
└─────────────────────────────────────┘
```

### Technology Stack

**Frontend:**
- Framework: React 18+ with TypeScript
- State Management: Redux Toolkit / Zustand
- UI Components: Ant Design / shadcn/ui
- Charts: Recharts / Chart.js
- Drag & Drop: react-beautiful-dnd

**Backend:**
- Framework: NestJS (Node.js + TypeScript)
- API: REST + GraphQL (Apollo)
- MCP: Official MCP SDK
- Authentication: Passport.js + JWT
- Validation: class-validator

**Database:**
- Primary: PostgreSQL 15+
- ORM: Prisma / TypeORM
- Vector: pgvector extension
- Cache: Redis 7+
- Search: PostgreSQL Full-Text Search

**AI Integration:**
- Anthropic Claude API
- OpenAI API
- LangChain for orchestration
- Custom prompt templates

**Infrastructure:**
- Hosting: AWS / Azure / GCP
- Containerization: Docker + Kubernetes
- CI/CD: GitHub Actions
- Monitoring: Prometheus + Grafana
- Logging: ELK Stack

---

## Constraints & Dependencies

### Technical Constraints
- Must support MCP protocol specification v1.0+
- PostgreSQL minimum version 14 (for pgvector)
- Node.js 18+ required
- Browser compatibility: Latest 2 versions of major browsers

### Business Constraints
- M1-M6 timeline: 10-12 months total
- Initial team size: 6-8 people (PM, Architect, 2 Backend, 1 Frontend, 1 AI Engineer, 1 QA)
- Budget: TBD based on cloud costs and AI API usage
- Private deployment option required for enterprise

### External Dependencies
- MCP protocol stability and adoption
- AI model API availability and pricing
- Third-party integration APIs (GitHub, Slack, etc.)
- Cloud provider service levels

### Risks & Mitigation
See separate Risk Assessment Report for detailed analysis.

---

## Competitive Analysis

### Comparison with Existing Solutions

| Feature | ColaFlow | Jira | Linear | Asana | GitHub Projects |
|---------|----------|------|--------|-------|-----------------|
| AI Integration | ⭐⭐⭐⭐⭐ | ⭐ | ⭐⭐ | ⭐⭐ | ⭐ |
| MCP Protocol | ⭐⭐⭐⭐⭐ | - | - | - | - |
| Agile Workflows | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ |
| Ease of Use | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ |
| Customization | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ |
| Pricing | TBD | ⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |

### Unique Differentiators
1. **MCP-Native**: First project management tool built on MCP protocol
2. **AI-First**: AI as a team member, not just a feature
3. **Human-in-Loop**: Secure AI operations with mandatory review
4. **Open Platform**: Extensible through MCP ecosystem
5. **Developer-Friendly**: API-first design, comprehensive SDK

---

## Future Roadmap (Post-M6)

### Phase 2 Enhancements
- Multi-AI agent collaboration (PM Agent, Dev Agent, QA Agent)
- IDE integration (VS Code, JetBrains)
- Mobile native apps (iOS, Android)
- Advanced analytics and predictive insights

### Phase 3 Ecosystem
- ColaFlow SDK for custom integrations
- Prompt Marketplace for community templates
- Plugin architecture for third-party extensions
- White-label solution for enterprise

### Phase 4 Scale
- Multi-tenant SaaS platform
- Enterprise feature set (SSO, LDAP, audit compliance)
- Global CDN for performance
- Regional data centers for compliance

---

## Appendices

### Glossary
- **MCP**: Model Context Protocol - open protocol for AI tool integration
- **Epic**: Large feature or initiative spanning multiple sprints
- **Story**: User-facing feature or requirement
- **Task**: Specific work item with clear deliverable
- **Sprint**: Time-boxed iteration (typically 2 weeks)
- **Diff Preview**: Visual comparison of current vs. proposed state

### References
- MCP Protocol Specification: https://modelcontextprotocol.io
- Project Vision Document: product.md
- Architecture Design: (To be created in M1)
- API Documentation: (To be created in M2)

### Revision History
| Version | Date | Author | Changes |
|---------|------|--------|---------|
| 1.0 | 2025-11-02 | ColaFlow PM Team | Initial PRD creation |

---

**Document Status:** Draft - Pending stakeholder review and approval

**Next Steps:**
1. Review with technical team for feasibility
2. Validate timeline and resource allocation
3. Finalize M1 sprint plan
4. Begin detailed technical design

