ColaFlow/docs/PRD.md

ColaFlow Product Requirements Document (PRD)

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

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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

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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)

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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

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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)

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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

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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

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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

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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.

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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

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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

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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

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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