Predictive Analytics
ML models that forecast what matters to your business - demand, churn, anomalies, and recommendations backed by data your team can trust.
Trusted by companies that ship production software on deadline
Everything you need, nothing you don't
We scope each engagement precisely so you get senior-level work on the capabilities that matter.
Demand forecasting
Predict sales, inventory needs, and resource demand - with seasonality, trends, and external signals baked into the models.
Churn prediction
Identify at-risk customers before they leave - with actionable scores and feature explanations your retention team can act on.
Anomaly detection
Flag unusual patterns in transactions, metrics, or system behavior - catching fraud, outages, and data quality issues early.
Recommendation engines
Personalized product, content, and action recommendations - collaborative filtering, content-based, or hybrid approaches.
A/B testing infrastructure
Experiment frameworks with statistical rigor - so you measure model and feature impact before rolling out to all users.
Model monitoring
Track prediction drift, data drift, and performance degradation - with alerts and retraining triggers when models go stale.
From historical data to actionable predictions
We explore your data, train models with proper validation, and deploy predictions into the systems your team already uses.
- Learn more
Week 1: Data exploration & target definition
We assess data quality, define prediction targets, and establish baseline metrics - with a clear success criteria before modeling.
- Learn more
Weeks 2-8: Model & deploy
Feature engineering, model training, and validation ship incrementally - each sprint improves accuracy on held-out test data.
- Learn more
Launch: Monitor & retrain
We deploy to production, add monitoring dashboards, and set up retraining pipelines so models stay accurate as data evolves.
Renting is local, so search had to understand a place and a date range as one question rather than two filters. Mirimera built the marketplace and the software our suppliers run on, and because it is one system underneath, nothing has ever had to be kept in sync.
- Pablo
CEO, Big RentalsML & analytics stack
Proven libraries for modeling, experiment tracking, and observability - so predictions are reproducible and production-ready.
scikit-learn
Comprehensive ML library for classification, regression, clustering, and preprocessing - the foundation of most tabular ML.
- XGB
XGBoost
Gradient boosting framework that consistently wins on structured data - fast training and strong out-of-the-box performance.
MLflow
Experiment tracking, model registry, and deployment - so every model version is logged, compared, and reproducible.
Python
The standard for data science workflows - pandas, numpy, and the entire ML ecosystem at your fingertips.
Jupyter
Interactive notebooks for exploration, prototyping, and sharing analysis - bridging data science and engineering.
Grafana
Dashboards and alerting for model performance, prediction latency, and data drift in production environments.
Ready to predict what matters?
Book a free 30-minute call. We'll review your data, define prediction targets, and outline a modeling approach with realistic accuracy goals.