Know What's Coming — Before It Arrives

Predictive Analytics Solutions

Machine learning models that forecast demand, predict churn, score leads, and surface opportunities before your competitors see them.

Overview

Reactive decisions cost money. Predictive decisions make money. We build and deploy machine learning models trained on your historical data to forecast what's going to happen next — whether that's which customers will churn, which leads will convert, or how much stock to order next month. Actionable predictions, not just interesting graphs.

Core Capabilities

Demand Forecasting

92%
Forecast Accuracy

Predict sales and inventory needs by SKU, region, and season — with confidence intervals to guide safe ordering decisions.

Churn Prediction Model

30 days
Early Warning

Identify which customers are likely to leave in the next 30 days so your retention team can act before it's too late.

Lead Scoring

40%
Higher Conversion

Rank inbound leads by conversion probability based on firmographics, behaviour signals, and historical patterns.

Anomaly Detection

Real-time
Anomaly Alerts

Real-time alerts when metrics deviate from expected patterns — catch fraud, equipment failure, or performance drops instantly.

Real-World Use Cases

FMCG Distribution

Over-ordering seasonal products leading to ₹25L in expired stock annually.

Demand forecasting model trained on 3 years of sales data, seasonality, and regional weather patterns.

Expired stock reduced by 80%. Stockout rate on fast-moving SKUs dropped to under 2%.
Telecom

High-value customers churning without warning — discovered only when they ported out.

Churn prediction model on usage patterns, support interactions, and plan tenure to flag at-risk accounts.

Retention team intervened with 68% of flagged customers. Churn reduced by 34%.
Insurance

Sales team spending equal time on every lead regardless of quality.

Lead scoring model based on demographics, past enquiries, and policy type interest to prioritise hot leads.

Sales team conversion rate improved from 12% to 19%. Call time on low-quality leads cut by 50%.

Our Process

01

Analyse

We assess your historical data quality, volume, and the business question to define the right model type.

02

Engineer

Feature engineering — the process of teaching the model which signals matter most.

03

Train

Model trained, validated, and benchmarked against a held-out test set before any production deployment.

04

Deploy

Model deployed as an API or embedded in your existing dashboard — and monitored for drift over time.

Technologies We Use

PythonScikit-learnXGBoostTensorFlowMLflowBigQueryGrafana

Ready to Get Started?

Let's build your predictive analytics solutions solution together.

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