Overview
Machine learning work spans a wide range, from a quick predictive model to a fully productionized system with monitoring and retraining built in. XFactr's India-based teams handle the full range, with particular depth in the data engineering and MLOps discipline that determines whether a model actually holds up in production.
Machine Learning Built for Production, Not Just a Notebook
A model that performs well in a notebook on a clean sample dataset often behaves very differently once it's exposed to real, messy production data. Our machine learning engagements are built around this reality: proper feature engineering, validation against realistic data, and a monitoring layer that catches accuracy drift before it becomes a business problem.
This machine learning work is closely tied to our broader custom AI & ML development and MLOps & DevOps practices, spanning our engineering teams across India.
What We Deliver
Predictive Modeling
Machine learning models built for forecasting, classification, or anomaly detection use cases.
Feature Engineering
The data preparation work that determines most of a model's real-world accuracy.
Model Deployment & MLOps
Getting models into production with proper monitoring and retraining discipline.
Machine Learning Consulting
Assessing whether a machine learning approach is the right fit for your problem.
Benefits
- Machine learning models built and validated against realistic, messy data.
- A production deployment approach, not just a proof-of-concept notebook.
- Ongoing monitoring that catches model drift before it affects business decisions.
- Engineering delivered by India-based teams with global delivery coordination.
How We Work
Problem & Data Assessment
Evaluating whether machine learning is the right approach for your problem.
Feature Engineering & Modeling
Building and validating models against real, representative data.
Deployment
Moving the model into a production-grade serving and monitoring environment.
Monitoring & Retraining
Ongoing tracking of model accuracy with a retraining process in place.
Industries We Serve
Technology & Expertise
Why XFactr.AI
This same machine learning discipline underpins our multimodal diagnostics work with Kongsberg Digital. See more in our case studies.
Our Presence in India
Our machine learning engineering teams are based in Bangalore and Mangalore, India, with delivery coordination from our Atlanta, Georgia team in the United States. This structure lets global clients draw on deep, specialized ML engineering talent in India while maintaining close collaboration through a U.S.-based delivery presence.
Frequently Asked Questions
How is a machine learning company different from a general software company?
A machine learning company has dedicated expertise in the full ML lifecycle, feature engineering, model training, validation, and MLOps, beyond general application development skills.
Do you build custom models, or use pre-built ones?
Both, depending on the use case. We evaluate whether a pre-built model or API is sufficient, or whether your problem genuinely requires a custom-trained model.
What industries does your machine learning team typically work in?
Manufacturing, energy, financial services, and retail, among others, particularly for predictive maintenance, forecasting, and anomaly detection use cases.
How do you make sure a model stays accurate after deployment?
Through production monitoring that tracks accuracy and data drift, paired with a defined retraining process rather than treating deployment as the end of the project.
Can your India-based team work directly with our in-house data science team?
Yes, we regularly work alongside existing internal data science and engineering teams rather than only as a standalone vendor.
Have a machine learning use case to evaluate?
We'll assess your data and use case, and give you an honest read on feasibility.
Talk to Our ML Team