At Galific, we build Custom Machine Learning Systems that are tailored to your specific business needs—not generic, pre-built tools. Whether you’re in finance, healthcare, e-commerce, or manufacturing, our goal is simple: help you use your own data to solve your biggest challenges and drive better ROI.
From detecting fraud and predicting churn to personalizing customer experiences and optimizing operations, our models are designed with your goals, your data, and your growth in mind.
Domain-Specific ML Models
We study your business and industry context before building any model—so your ML solution actually aligns with your outcomes.
Data Preprocessing & Feature Engineering
We clean, normalize, and extract the most relevant features from your raw datasets to boost accuracy and relevance.
Model Training, Validation & Hyperparameter Tuning
Every model is trained using the latest techniques and tested rigorously before deployment.
Continuous Learning & Model Optimization
Post-deployment, we help your models improve through feedback loops, updated data, and periodic retraining.
Explainability & Transparency
We build models that are interpretable—so stakeholders know why a prediction is made, not just what it is.
We are here to help our customer any time. You can call on 24/7 To Answer Your Question.
Off-the-shelf tools often try to be “one size fits all,” which means they’re not optimized for your specific data, goals, or industry nuances. With Galific’s custom ML systems, you get models that are trained specifically on your historical data, tuned for your KPIs, and built to solve your unique problems. That means better accuracy, stronger ROI, and more control over the entire pipeline.
Yes! In fact, most businesses start there. Our team helps you audit your current data, clean it, handle missing values, and even label it if needed (manually or using semi-supervised techniques). We also help build data pipelines that keep your future data organized and model-ready.
The timeline depends on your use case and data readiness. A typical project takes 4–12 weeks:
Week 1–2: Data discovery & cleaning
Week 3–6: Model building & testing
Week 7–10: Integration & deployment
Week 11–12: Performance monitoring & feedback loop setup
We provide you with a roadmap and checkpoints throughout the journey.
That’s a great question—and we’ve got it covered. We implement model monitoring and performance alerts, so we can detect when accuracy drops. We also offer scheduled retraining, where your model is re-fed with new data periodically to stay updated with business changes or customer behavior shifts.
Absolutely. We prioritize explainability using techniques like SHAP values, decision trees, and feature importance plots. This way, even your non-technical stakeholders can understand how the model arrived at its prediction—vital for trust, compliance, and decision-making.
We offer end-to-end post-deployment support, including:
Real-time performance tracking dashboards
We act as your long-term AI partner, not just one-time builders.
Automated alerts for performance dips
Version control and rollback options
Scheduled model evaluations
Retainer-based support for tuning, optimization, and scaling
