Enterprise Predictive Analytics & ML Modeling | Forecasting & Decision Intelligence

Predictive Analytics & Decision Intelligence

Move from “What happened?” to “What will happen next?” We build rigorous Machine Learning Models that turn historical data into actionable foresight—enabling enterprises to forecast demand, quantify risk, and optimize resource allocation with mathematical precision.

Stop Reacting. Start Anticipating.

Operational Efficiency

Predict inventory shortages and equipment failures before they impact the bottom line.

Precision Targeting

Identify high-LTV customers and churn risks with granular segmentation models.

Explainable AI (XAI)

We don't build black boxes. Our models use SHAP/LIME values to explain why a prediction was made, ensuring regulatory compliance.

Our Machine Learning Capabilities

Time-Series Forecasting

Demand & Revenue Planning.

Multi-variate forecasting (holidays, weather, promos).
Hierarchical forecasting (Global -> Regional -> SKU level).
Models: Prophet, ARIMA, LSTM, Temporal Fusion Transformers.

Risk & Fraud Modeling

Real-time Anomaly Detection.

Transaction Fraud Scoring.
Credit Risk & Loan Default Prediction.
Synthetic Identity Detection.
Models: Isolation Forests, Autoencoders, XGBoost.

Recommendation Engines

Personalization at Scale.

Collaborative Filtering & Content-Based Filtering.
“Next Best Action” prediction for Sales teams.
Real-time session-based recommendations.
Models: Matrix Factorization, Deep Learning Recommenders.

Customer Behavior Analytics

Churn, LTV, and Segmentation.

Customer Lifetime Value (CLV) prediction.
Propensity to Buy / Propensity to Churn scoring.
Micro-segmentation clustering (K-Means).

Dynamic Pricing Engines

Margin Optimization.

Elasticity modeling based on demand signals.
Competitor price scraping & reaction logic.
Inventory-based pricing adjustments.

Computer Vision (Visual Inspection)

Automated Quality Control.

Defect detection in manufacturing.
Object counting and classification.
Models: YOLO v8, ResNet, Vision Transformers.

The Data Science Toolkit

We select the right algorithm for the data distribution.

Classic ML

Deep Learning

Time-Series

Explainability

From Raw Data to Real-Time Inference

Our standard pipeline for productionizing predictive models.

ML in Production: Real Results

Demand Forecasting System

Built a hierarchical forecasting model for 50,000 SKUs, reducing inventory holding costs by 18% while improving stock availability.

Credit Risk Scoring Engine

Developed an XGBoost-based credit scoring model that reduced default rates by 12% compared to legacy scorecards.

Churn Prediction Platform

Created a propensity model identifying at-risk customers 30 days in advance, allowing CS teams to save $1.2M in ARR.

Solutions Powered by Predictive ML

We apply these models to solve specific industry challenges.

Demand Forecasting

Predict sales to optimize inventory.

Route Optimization

Predict traffic and delivery times.

Lead Scoring

Predict which leads will close.

Fraud Detection

Real-time transaction scoring.

Infrastructure Questions

How much historical data do we need?

It depends on the complexity. For time-series forecasting, we ideally need 2+ years of history to capture seasonality. For classification tasks (like churn), 6-12 months is often sufficient.

How do you handle "Black Box" models?

We prioritize Explainable AI (XAI). We use SHAP (Shapley Additive Explanations) values to tell you exactly which features (e.g., “Transaction Size” or “Location”) contributed to a specific prediction.

Do you support Real-Time Inference?

Yes. While many predictive models run in batch (overnight), we can deploy lightweight models (like LightGBM or ONNX-converted models) as APIs for <50ms real-time scoring.

Delivering AI Solutions Across the Globe

Ready to See into the Future?

Turn your data lake into a decision engine.