AI-powered consulting platform for small e-commerce and retail businesses. Automated insights for forecasting, inventory optimization, and churn prediction.

• Industries: E-commerce + Retail (healthcare Phase 2)
• Data Sources: Shopify/WooCommerce, Google Analytics 4, Stripe/PayPal, QuickBooks/Xero
• Core Use Cases:
  • Sales forecasting at SKU/store level
  • Inventory optimization (stockout/overstock alerts)
  • Customer churn prediction and segmentation

ai-consulting-platform/
├── ingestion/          # Data connectors for Shopify, GA4, QuickBooks, etc.
│   ├── shopify/
│   ├── ga4/
│   ├── quickbooks/
│   └── stripe/
├── models/             # ML models for forecasting, churn, inventory
│   ├── forecasting/
│   ├── churn/
│   └── inventory/
├── api/                # REST API (FastAPI/Flask)
│   ├── endpoints/
│   └── schemas/
├── ui/                 # Dashboard (React or embedded analytics)
│   ├── components/
│   └── views/
├── infra/              # Infrastructure as code (Docker, K8s)
│   ├── docker/
│   └── kubernetes/
├── notebooks/          # Jupyter notebooks for experimentation
└── tests/              # Unit and integration tests

• Data Ingestion: REST API connectors, scheduled pulls (daily/hourly)
• Stream Processing: Apache Kafka + Apache Flink
• Data Warehouse: BigQuery or Snowflake
• ML Framework: Python (scikit-learn, XGBoost, Prophet, LightGBM)
• Model Tracking: MLflow
• API: FastAPI or Flask
• Deployment: Docker + Kubernetes (GKE/EKS) or Cloud Run/Lambda
• Dashboard: Metabase, Superset, or custom React
• Alerting: SendGrid/Mailgun (email), Slack webhooks
• Security: TLS 1.3, AES-256, OAuth 2.0, SOC 2 roadmap

• Connect data sources for 5-10 pilot customers
• Establish baseline metrics (forecast error, inventory issues, churn)

• Deploy forecasting, inventory, churn models
• Start weekly reports + anomaly alerts
• Collect qualitative feedback

• Tune models per client
• Implement experiments (reorder rules, win-back campaigns)
• Document 3-5 case studies with before/after metrics

• Inputs: Historical sales, promotions, seasonality, marketing data
• Output: 4-8 week demand forecasts with accuracy tracking

• Inputs: Forecasts + on-hand inventory + lead times
• Output: Reorder suggestions, stockout/overstock alerts, safety-stock guidance

• Inputs: Order history, visit behavior, email engagement
• Output: At-risk segments, loyal segments, suggested actions

• Starter Pilot: $150/month (1 store, email reports + dashboard)
• Growth Pilot: $400/month (3 stores, custom alerts + monthly review)

# Clone the repository
git clone https://github.com/labgadget015-dotcom/ai-consulting-platform.git
cd ai-consulting-platform

# Set up virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run tests
pytest tests/

• Forecast accuracy improvement: 20-30%
• Cost reduction: 15-25%
• Churn reduction: 10-20% within 6 months

• TLS 1.3 encryption in transit
• AES-256 encryption at rest
• OAuth 2.0 authentication
• SOC 2 Type II compliance roadmap
• GDPR/CCPA compliant

MIT License - see LICENSE file for details

This is a private project for pilot customers. Contact the team for collaboration opportunities.

Status: V1 Development | Target: 5-10 pilot customers by Q1 2026

This project is part of a connected suite of AI tools: