Predictive Intelligence

ML Engineering

Turn Your Data Into Predictions That Drive Decisions

Machine Learning isn't magic—it's pattern recognition at scale. We build custom ML systems that analyze your historical data to predict future outcomes: which customers will churn, what products will sell, when equipment will fail. Predictions you can act on, not just reports you file away.

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What This Means For Your Business

Custom Prediction Models

Models trained specifically on YOUR data for YOUR problems. Not generic off-the-shelf algorithms—custom solutions that understand the nuances of your business and industry.

Real-Time Scoring

Get predictions instantly when you need them—not in a batch report tomorrow. Score a lead the moment they visit your site. Detect anomalies as they happen.

Automatic Retraining

Markets change, customer behavior evolves. Your models automatically retrain on new data to stay accurate, without manual intervention or data science bottlenecks.

Explainable Predictions

Know WHY the model made a prediction, not just what it predicted. Essential for regulatory compliance, customer explanations, and building trust in AI decisions.

Performance Monitoring

Dashboards showing model accuracy over time, prediction distributions, and drift alerts—so you know immediately if the model starts degrading.

Enterprise Integration

Predictions flow directly into your existing systems—CRM, ERP, marketing automation—triggering actions automatically, not sitting in a separate tool.

Real-World Examples

See how this technology solves actual business problems

Customer Churn Prediction & Prevention

SaaS & Subscription

Before

You only discover customers are leaving when they cancel. Win-back campaigns have 5% success rates. Customer success is reactive—putting out fires instead of preventing them. Annual churn is 15%+.

With Our Solution

ML model trained on your customer data predicts churn risk 60-90 days in advance, identifying specific warning signs (decreased usage, support tickets, payment issues). High-risk customers are automatically flagged for proactive outreach.

Result

Churn reduced by 25%. Customer success team focuses efforts where they matter most. Predicted customers saved at 40% rate vs 5% for reactive outreach.

Demand Forecasting & Inventory Optimization

Retail & E-commerce

Before

Buyers rely on last year's numbers plus gut feel. Stockouts on hot items lose sales. Overstock on slow movers requires markdowns. $2M+ tied up in excess inventory at any given time.

With Our Solution

ML forecasting combines your sales history with external signals—weather, events, competitor pricing, economic indicators—to predict demand at SKU-location level. Automatic reorder points and safety stock calculations.

Result

Stockouts reduced by 35%. Inventory carrying costs down 20%. Forecast accuracy improved from 65% to 90%+. Buyers focus on strategy, not spreadsheets.

Predictive Lead Scoring

B2B Sales

Before

Sales reps treat all leads equally or use simplistic scoring (company size + title = score). They waste time on leads that will never convert while hot prospects go cold waiting for follow-up.

With Our Solution

ML model analyzes thousands of data points—firmographics, behavior patterns, engagement history, similar customer profiles—to score leads by actual likelihood to convert and predicted deal size.

Result

Sales efficiency up 35%—reps focus on leads most likely to close. Conversion rates increased 50%. Time-to-contact for hot leads reduced from days to hours.

How We Build It

Our proven process for delivering production-ready solutions

Data & Problem Discovery

We analyze your data assets, define the prediction problem precisely (What are we predicting? What actions will predictions trigger?), and assess feasibility before writing any code.

Model Development & Validation

We engineer features, train and compare model architectures, and rigorously validate on holdout data—ensuring the model performs well on data it hasn't seen, not just training data.

Production Deployment & Monitoring

We deploy models with real-time serving capability, integrate with your systems, set up monitoring dashboards, and establish automated retraining pipelines.

Technologies We Use

Enterprise-grade tools and frameworks powering your solution

Model Training & Deployment

The infrastructure for building and serving predictions at scale

PyTorch / TensorFlow — Industry-standard frameworks for training custom machine learning models

AWS SageMaker — Managed platform for training, deploying, and scaling ML models in production

MLflow — Tracks experiments, manages model versions, and handles deployment lifecycle

Data & Feature Engineering

How we prepare your data to make accurate predictions

Apache Spark — Processes massive datasets for training data preparation and feature engineering

Feast — Feature store ensuring consistent data between training and real-time prediction

dbt — Transforms raw data into clean, reusable features for model training

Ready to Build?

Let's discuss how we can apply this technology to solve your specific business challenges.

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