Documentation

Detailed technical breakdown of the EcoGenius platform capabilities, architectures, and implementation strategies.

AI Demand Forecasting

What Problem It Solves

Stockouts & overstock
Poor visibility of future demand
Manual spreadsheet forecasting
Can't react to promotions/events

Predicts demand at SKU/store/region level.

Architecture

Data → Features → Models → Forecast API → Monitoring

Data Sources

Sales history
Prices/promotions
Inventory
Holidays/events
Weather/external signals

Data Processing

Validation
Missing values
Outlier detection
Time aggregation

Model Strategy & Code

We use a hybrid approach combining ARIMA, XGBoost/LightGBM, and LSTM/TFT models.

# Feature Engineering Example
df['lag_7'] = df['sales'].shift(7)
df['lag_30'] = df['sales'].shift(30)
df['rolling_mean_7'] = df['sales'].rolling(7).mean()

# Model Training
model = XGBRegressor(n_estimators=300, learning_rate=0.05, max_depth=6)
model.fit(X_train, y_train)

Business Impact

20–35% accuracy improvement

Lower inventory cost

Better promotion planning

Faster demand reaction

Dynamic Pricing Intelligence

Overview

An AI system that automatically adjusts prices in real time using demand signals, competition, customer behavior, and business constraints.

Pricing Model Strategy

Key Features

Demand elasticity
Inventory pressure
Time-based signals
Competitor price gap
Promotions

Hybrid Approach

Rule-based guardrails
ML models (XGBoost, LightGBM)
Elasticity-based optimization
Reinforcement learning

Implementation Logic

def recommend_price(base_price, elasticity, min_price, max_price):
    adjusted_price = base_price * (1 + elasticity)
    return max(min(adjusted_price, max_price), min_price)

# API Endpoint Example
@app.get("/price")
def get_price(product_id: str):
    return {"product_id": product_id, "recommended_price": 499}

Business Impact

5–15% revenue uplift

Higher conversion

Faster market reaction

Lower manual work

Carbon-Smart Route Optimization

Goal: Lowest-carbon, cost-efficient routing

An AI-powered system that identifies the most efficient transportation routes by optimizing distance, travel time, fuel consumption, and carbon emissions.

System Architecture

Location Data → Constraints → Optimization Engine → Route API → Monitoring & Reporting

Optimization Logic

We use multi-objective optimization (Min CO2, Min time, Min fuel cost) with graph-based algorithms and linear programming.

# Scoring Function
def route_score(distance, time, co2, w_dist=0.3, w_time=0.3, w_co2=0.4):
    return (w_dist * distance) + (w_time * time) + (w_co2 * co2)

# Emission Calculation
routes['fuel_used'] = routes['distance_km'] * routes['fuel_rate_l_per_km']
routes['co2_kg'] = routes['fuel_used'] * 2.31

Key Outcomes

5–20% fuel reduction

Lower CO2 emissions

Better on-time delivery

Reduced logistics costs

Risk Management & Scenario Planning

Overview

AI-driven decision intelligence that helps organizations anticipate uncertainty, quantify risk exposure, and prepare optimal responses before disruptions occur.

Solution Capabilities

1. Risk Identification & Classification

Market risk, Operational risk, Financial risk, Regulatory & compliance risk, Environmental & ESG risk.

2. Scenario Modeling (What-If Analysis)

Simulate demand drops (20-40%), supplier failures, cost inflation spikes, and geopolitical disruptions.

3. Predictive & Probabilistic Modeling

Time-series forecasting, Monte Carlo simulations, Bayesian inference, and Stress testing models.

Simulation Example

# Monte Carlo Simulation
import numpy as np
revenue_mean = 10_000_000
revenue_std = 1_500_000
simulations = np.random.normal(revenue_mean, revenue_std, 10000)
worst_case = np.percentile(simulations, 5)
best_case = np.percentile(simulations, 95)

# Risk Scoring Logic
risk_score = probability * impact
priority = "High" if risk_score > 0.7 else "Medium" if risk_score > 0.4 else "Low"

Computer Vision for Warehouse & Quality

Overview

AI-powered visual intelligence solution that uses cameras and deep learning models to monitor operations, detect defects, track inventory, and ensure quality compliance in real time.

Key Capabilities

1Visual Inspection & Defect Detection
2Warehouse Monitoring & Inventory Tracking
3Process Compliance Monitoring
4Safety & Anomaly Detection

AI Models & Code

Uses Convolutional Neural Networks (CNNs), Object Detection (YOLO, Faster R-CNN), and Instance Segmentation.

# Object Detection with YOLO
import cv2
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
frame = cv2.imread("warehouse.jpg")
results = model(frame)
results[0].show()

# Quality Check Logic
if detected_defect:
    quality_status = "Fail"
    alert_team()
else:
    quality_status = "Pass"