The difference between companies that lead and companies that follow often comes down to one thing: the ability to see what's coming. While your competitors are reacting to last month's numbers, you could be acting on next quarter's predictions.
We build machine learning models that run in production—not in PowerPoints. Models that predict which customers will leave, which products will sell, which transactions are fraudulent, and which machines will fail. Every model comes with the infrastructure to monitor, retrain, and improve over time.
Go beyond traditional bureau scores with ML models that incorporate alternative data sources. We build credit scoring systems that reduce default rates by 30-50% while maintaining regulatory compliance. Every model includes explainability (SHAP values) so you can justify decisions to auditors and customers, plus continuous monitoring to catch drift before it impacts performance.
Identify customers at risk of leaving 3-6 months before they do. Our churn models analyze behavioral patterns—usage changes, support interactions, payment delays—to flag accounts that need attention. We integrate directly with your CRM so your retention team gets actionable alerts, not just reports. Clients typically see 15-25% improvement in retention rates.
Replace gut feelings and spreadsheet extrapolations with ML-powered demand forecasts. We build models that incorporate seasonality, promotions, holidays, weather, and economic indicators to predict demand by product, region, and channel. The result: less overstock tying up capital, fewer stockouts losing sales, and a supply chain that actually responds to reality.
Catch fraud in real-time, not in monthly reconciliations. We deploy anomaly detection systems that analyze transaction patterns and flag suspicious activity before money leaves the account. Our hybrid approach combines ML models with business rules you control, reducing false positives while catching the fraud that rule-based systems miss.
Stop fixing equipment after it breaks. Our predictive maintenance models analyze sensor data, vibration patterns, temperature readings, and maintenance history to predict failures before they happen. Prioritize your maintenance schedule based on actual risk, extend equipment lifespan, and reduce unplanned downtime by 25-40%.
Move beyond basic demographics to segments that actually predict behavior. We use clustering algorithms and behavioral analysis to group customers by how they act, not just who they are—purchase patterns, channel preferences, lifetime value trajectories, response to promotions. The output: segments your marketing team can actually use to personalize campaigns and allocate resources.
Show customers what they want before they search for it. We build recommendation engines using collaborative filtering, content-based methods, and deep learning—depending on what your data supports. Whether you're recommending products, content, or services, we implement A/B testing frameworks to continuously optimize performance and measure incremental revenue.
Python
R Language
Conda
Azure ML
TensorFlow
PyTorch
Scikit-Learn
SageMaker
WekaLet's discuss which predictions would move the needle for your business.
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