Building a comprehensive AI-powered management system that completely replaces traditional Product/Project Managers using DeepSeek-R1-8B model. The system automates task management, risk analysis, team coordination, and decision-making processes.
- Landing Page - Modern, responsive design with smooth animations
- Authentication System - Sign up/Sign in with social auth options
- Dashboard Interface - Comprehensive project overview with real-time metrics
- AI Chat Interface - Direct interaction with DeepSeek-R1 for task management
- Responsive Design - Optimized for all screen sizes
# Required dependencies
pip install fastapi uvicorn sqlalchemy psycopg2-binary redis celery
pip install python-multipart python-jose[cryptography] passlib[bcrypt]
pip install httpx pydantic-settings
# Project structure
backend/
├── app/
│ ├── __init__.py
│ ├── main.py
│ ├── core/
│ │ ├── config.py
│ │ ├── security.py
│ │ └── database.py
│ ├── api/
│ │ ├── __init__.py
│ │ ├── auth.py
│ │ ├── projects.py
│ │ ├── tasks.py
│ │ ├── ai.py
│ │ └── integrations.py
│ ├── models/
│ │ ├── user.py
│ │ ├── project.py
│ │ └── task.py
│ ├── services/
│ │ ├── ai_service.py
│ │ ├── slack_service.py
│ │ └── jira_service.py
│ └── utils/
│ ├── ollama_client.py
│ └── helpers.py
├── requirements.txt
└── docker-compose.yml# ollama_client.py
import httpx
from typing import Dict, List, Optional
class OllamaClient:
def __init__(self, base_url: str = "http://localhost:11434"):
self.base_url = base_url
async def generate_response(self, prompt: str, model: str = "deepseek-r1:8b") -> str:
"""Generate AI response using DeepSeek R1 model"""
async with httpx.AsyncClient() as client:
response = await client.post(
f"{self.base_url}/api/generate",
json={
"model": model,
"prompt": prompt,
"stream": False
}
)
return response.json()["response"]
async def analyze_project_risk(self, project_data: Dict) -> Dict:
"""AI-powered project risk analysis"""
prompt = f"""
As an expert project manager, analyze the following project data and provide risk assessment:
Project: {project_data['name']}
Timeline: {project_data['timeline']}
Team Size: {project_data['team_size']}
Progress: {project_data['progress']}%
Provide:
1. Risk score (1-10)
2. Top 3 risk factors
3. Specific recommendations
4. Resource allocation suggestions
"""
response = await self.generate_response(prompt)
return self.parse_risk_analysis(response)# models/project.py
from sqlalchemy import Column, Integer, String, Float, DateTime, Text, ForeignKey
from sqlalchemy.orm import relationship
from app.core.database import Base
class Project(Base):
__tablename__ = "projects"
id = Column(Integer, primary_key=True, index=True)
name = Column(String, index=True)
description = Column(Text)
status = Column(String, default="planning")
priority = Column(String, default="medium")
progress = Column(Float, default=0.0)
ai_risk_score = Column(Float, default=0.0)
ai_recommendations = Column(Text)
# Relationships
tasks = relationship("Task", back_populates="project")
team_members = relationship("ProjectMember", back_populates="project")
class Task(Base):
__tablename__ = "tasks"
id = Column(Integer, primary_key=True, index=True)
project_id = Column(Integer, ForeignKey("projects.id"))
title = Column(String, index=True)
description = Column(Text)
assignee_id = Column(Integer, ForeignKey("users.id"))
status = Column(String, default="todo")
priority = Column(String, default="medium")
estimated_hours = Column(Float)
ai_generated = Column(Boolean, default=False)
ai_complexity_score = Column(Float)
# Relationships
project = relationship("Project", back_populates="tasks")
assignee = relationship("User", back_populates="assigned_tasks")# services/slack_service.py
from slack_sdk.web.async_client import AsyncWebClient
from slack_sdk.errors import SlackApiError
class SlackService:
def __init__(self, token: str):
self.client = AsyncWebClient(token=token)
async def send_ai_update(self, channel: str, message: str):
"""Send AI-generated project updates to Slack"""
try:
response = await self.client.chat_postMessage(
channel=channel,
text=message,
username="Fevl AI Manager",
icon_emoji=":robot_face:"
)
return response
except SlackApiError as e:
print(f"Error sending message: {e}")
async def create_task_from_slack(self, message: str) -> Dict:
"""Parse Slack message and create task using AI"""
ai_prompt = f"""
Parse this Slack message and extract task information:
"{message}"
Extract and return JSON:
{{
"title": "task title",
"description": "detailed description",
"priority": "high/medium/low",
"estimated_hours": number,
"assignee_suggestion": "team member name if mentioned"
}}
"""
# Process with AI and return structured data
return await self.ai_service.parse_task_creation(ai_prompt)# websocket_manager.py
from fastapi import WebSocket
from typing import List, Dict
import json
class ConnectionManager:
def __init__(self):
self.active_connections: List[WebSocket] = []
async def connect(self, websocket: WebSocket):
await websocket.accept()
self.active_connections.append(websocket)
async def disconnect(self, websocket: WebSocket):
self.active_connections.remove(websocket)
async def broadcast_ai_update(self, data: Dict):
"""Broadcast AI-generated updates to all connected clients"""
message = json.dumps({
"type": "ai_update",
"data": data,
"timestamp": datetime.utcnow().isoformat()
})
for connection in self.active_connections:
try:
await connection.send_text(message)
except:
await self.disconnect(connection)- Set up FastAPI server with authentication
- Implement database models and migrations
- Create basic CRUD operations for projects/tasks
- Set up Ollama integration for DeepSeek-R1
- Implement AI task creation and assignment
- Build risk analysis and recommendation engine
- Create automated project monitoring
- Develop natural language processing for requirements
- Slack bot for team communication
- Jira API integration for issue tracking
- GitHub integration for code management
- Calendar APIs for meeting scheduling
- Real-time collaboration features
- Advanced analytics and reporting
- Automated sprint planning
- Performance optimization
# Start Ollama with DeepSeek R1
ollama run deepseek-r1:8b
# Start backend
cd backend
uvicorn app.main:app --reload --port 8000
# Start frontend
cd frontend
npm start
# Start Redis (for caching and sessions)
redis-server
# Start Celery (for background tasks)
celery -A app.worker worker --loglevel=info# docker-compose.yml
version: '3.8'
services:
frontend:
build: ./frontend
ports:
- "3000:3000"
environment:
- REACT_APP_API_URL=http://backend:8000
backend:
build: ./backend
ports:
- "8000:8000"
environment:
- DATABASE_URL=postgresql://user:pass@db:5432/fevl
- REDIS_URL=redis://redis:6379
- OLLAMA_URL=http://ollama:11434
depends_on:
- db
- redis
- ollama
ollama:
image: ollama/ollama
ports:
- "11434:11434"
volumes:
- ollama_data:/root/.ollama
db:
image: postgres:13
environment:
- POSTGRES_DB=fevl
- POSTGRES_USER=user
- POSTGRES_PASSWORD=pass
volumes:
- postgres_data:/var/lib/postgresql/data
redis:
image: redis:alpine
ports:
- "6379:6379"
volumes:
ollama_data:
postgres_data:- AI Accuracy: Task creation success rate, risk prediction accuracy
- Team Productivity: Task completion times, project delivery rates
- User Engagement: Daily active users, AI interaction frequency
- System Performance: Response times, uptime, error rates
- JWT-based authentication with refresh tokens
- Rate limiting for AI endpoints
- Input validation and sanitization
- Secure API key management for integrations
- Role-based access control (RBAC)
- React Native app for mobile access
- Offline capability for core features
- Push notifications for AI alerts
- Voice commands for task creation
This implementation plan provides a comprehensive roadmap for building your AI-powered management system. The frontend is already complete, and the backend architecture is designed to scale with your needs while maintaining high performance and reliability.