Optivize - Technical Guide

Optivize - Quick Start Technical Guide

🤖 FEATURE 1: AI Product Predictor

Technical Implementation

  • Frontend: Event-driven JavaScript with debounced input validation and progressive form enhancement
  • Data Flow: User Input → JSON Validation → API POST → ML Pipeline → Feature Engineering → Model Training/Inference → Response Rendering
  • ML Model: Scikit-learn RandomForestRegressor with feature importance and cross-validation

API Design

POST /api/train
{ samples: [{product_type, seasonality, price, marketing, distribution_channels, success_score}] }
POST /api/predict
{ product_type, seasonality, price, marketing, distribution_channels }
GET /api/history
Returns paginated prediction history with filtering

Smart Parts

  • Feature Engineering: Categorical encoding (one-hot), numerical scaling (StandardScaler), temporal encoding for seasonality
  • Real-time insights: Price positioning vs. market, marketing effectiveness scores, seasonality impact analysis

📦 FEATURE 2: Smart Inventory Manager

Technical Implementation

  • Database Schema: Hierarchical groups and items with foreign key relationships enforcing referential integrity
  • Frontend State Management: Custom observer pattern for reactive UI updates
  • Data Flow: Optimistic UI update → API Call → Server validation → DB transaction → Response → UI confirmation

API Endpoints

GET /api/deck
POST /api/deck
GET /api/deck/{id}?include_cards=true
PUT/DELETE /api/flashcard/{id}

Smart Parts

  • Optimistic UI updates for instant feedback
  • Relational integrity via CASCADE deletes to prevent orphaned items
  • Efficient queries using SQL JOINs to load groups with item counts

📊 FEATURE 3: Google Sheets Import

Technical Implementation

  • OAuth2 Flow: PKCE implementation for secure authorization without client secrets
  • Data Pipeline: Sheet ID → OAuth redirect → Token exchange → Sheets API → Data transformation → Bulk insert → UI refresh
  • Session management: OAuth state stored temporarily with CSRF protection and cleanup

API Architecture

GET /google/connect
POST /google/import
Callback: /auth/callback?code=xxx&state=xxx

Smart Parts

  • Minimal OAuth scopes (read-only) and CSRF prevention via state parameter
  • Intelligent column mapping detects quantity and description formats
  • Error handling for expired tokens, malformed sheets, and network failures with user feedback

🚨 FEATURE 4: Smart Alerts (Zapier Integration)

Technical Implementation

  • Webhook REST endpoints optimized for Zapier polling every 15 minutes
  • Dynamic URL generation with proper encoding for phone numbers and thresholds
  • Notification payloads vary by channel: email, SMS, or both

API Endpoints

GET /api/zapier/low-stock/{item_id}/{threshold}
GET /api/zapier/low-stock-sms/{item_id}/{threshold}?phone=+1234567890
GET /api/zapier/low-stock-both/{item_id}/{threshold}?phone=xxx

Smart Parts

  • Minimized unnecessary processing by returning empty responses when thresholds not breached
  • Consistent backend logic generates multi-channel message formats
  • International phone validation with country code detection and SMS provider compatibility

📅 FEATURE 5: Smart Calendar

Technical Implementation

  • Frontend: Custom JavaScript calendar widget with event drag/drop and responsive design
  • Backend: RESTful API supporting CRUD operations for events linked to user IDs
  • Data Model: Events include title, description, start/end datetime, reminder settings, and category tags
  • Synchronization: Option to sync with Google Calendar API via OAuth2

API Endpoints

GET /api/calendar/events?user_id=123&date=YYYY-MM-DD
POST /api/calendar/events
PUT /api/calendar/events/{event_id}
DELETE /api/calendar/events/{event_id}

Smart Parts

  • Real-time conflict detection with visual feedback
  • Reminder notifications via email or SMS integrated with the alerts system
  • Category color-coding for quick visual parsing
  • Offline mode support with localStorage caching and sync on reconnect

🔧 System Architecture & Data Flow

  • Frontend: Jekyll + Vanilla JS
  • Backend: Python Flask API Gateway
  • Database: SQLite/PostgreSQL managed with migrations
  • External Integrations: Google Sheets OAuth2, Zapier Webhooks
  • ML Pipeline: Scikit-learn RandomForest with GridSearchCV tuning

Database Schema

Users: id, username, email, created_at
Groups: id, title, user_id, created_at
Items: id, title, content, deck_id, user_id, created_at
Predictions: id, user_id, product_data, score, created_at
Models: id, user_id, model_data, accuracy_metrics, created_at
Events: id, user_id, title, description, start_time, end_time, category, reminder, created_at

API Response Pattern

{
  "success": true,
  "data": {...},
  "message": "Operation completed",
  "metadata": { "timestamp": "...", "user_id": "..." }
}

🤖 Machine Learning Deep Dive

Model Training Pipeline

  • JSON schema validation, outlier detection, completeness checks
  • Feature engineering: one-hot encoding, StandardScaler, temporal features
  • Model: RandomForestRegressor with hyperparameter tuning (GridSearchCV)
  • Validation: K-fold cross-validation, R² scoring, MAE calculation
  • Persistence: Joblib serialization with versioning for rollback

Prediction Analytics Engine

feature_weights = {
  'price_position': model.feature_importances_[0],
  'marketing_effectiveness': model.feature_importances_[1],
  'distribution_reach': model.feature_importances_[2],
  'seasonality_match': model.feature_importances_[3]
}
insights = {
  'price_analysis': calculate_market_positioning(price, category),
  'marketing_score': assess_marketing_effectiveness(marketing_level),
  'seasonality_impact': analyze_seasonal_patterns(product_type, season),
  'recommendations': generate_actionable_advice(prediction, feature_weights)
}