ML & Predictive Models
Your data already has the answers. We teach your software to find them.
From forecasting and anomaly detection to recommendation engines, we build and maintain custom machine learning models trained on your data — and keep them accurate as your business evolves.
Talk to us about thisThe gap between data and decisions
Most businesses are sitting on years of data and still making decisions by gut. Spreadsheets get exported, someone eyeballs a trend, and a call is made.
We close that gap with models that surface the right signal at the right moment — embedded directly into your systems and workflows, not waiting in a report no one reads.
What changes
- Reactive→Predictive
Find problems before they find you
- Manual→Automated
Models score, classify, and flag at scale
- One-size→Personalized
Every customer gets a different experience
Models we build and deploy
Demand & Sales Forecasting
Predict future demand, revenue, and inventory needs using historical patterns, seasonality, and external signals — so you plan with precision, not guesswork.
Anomaly & Fraud Detection
Identify outliers, suspicious patterns, and operational failures the moment they appear — before they become expensive problems.
Customer Churn Prediction
Score customers by retention risk and trigger proactive interventions before they leave — turning reactive support into predictive retention.
Recommendation Engines
Deliver personalized product, content, and action recommendations at scale — trained on real user behavior, not editorial guesses.
Risk Scoring & Classification
Classify leads, applications, events, and entities automatically — with models calibrated to your data, your risk tolerance, and your business rules.
Model Monitoring & Retraining
Track prediction drift and data degradation in production, and retrain automatically when accuracy drops — so models stay sharp as your business evolves.
From raw data to production model
Data audit & problem framing
We start by understanding what data you actually have, what decisions you want to improve, and whether a model can meaningfully close that gap — before writing a single line of training code.
Feature engineering
We transform raw tables, logs, and events into the signals that drive prediction quality — handling missing values, encoding, aggregations, and domain-specific features.
Training & validation
We train, tune, and validate models rigorously against held-out data — selecting architectures that balance accuracy, interpretability, and inference cost.
Deploy & integrate
We serve models via API or embedded pipeline — connected to your product, CRM, or data stack so predictions reach decisions immediately.
Monitor & retrain
We instrument models in production, track drift and accuracy over time, and retrain on a schedule or threshold.
Industry applications
Retail & E-commerce
- SKU-level demand forecasting for inventory optimization
- Personalized product recommendations based on browsing and purchase history
- Churn scoring and re-engagement campaign targeting
Finance & Insurance
- Credit and loan default risk scoring
- Real-time fraud and transaction anomaly detection
- Claims severity prediction and triage routing
Operations & Logistics
- Predictive maintenance for equipment and infrastructure
- Delivery time and capacity forecasting
- Supplier risk classification and supply chain anomaly detection
SaaS & Digital Products
- Feature adoption scoring and upsell opportunity prediction
- Usage anomaly detection and early warning for at-risk accounts
- Search ranking and personalization models embedded in-product
Models are only as good as the decisions they reach.
We don't build models to sit in a notebook. Every model we ship is connected to a real decision surface — an API, a dashboard, a user-facing feature — and instrumented to prove its value in production.