Machine Learning
Predictive models that turn data into decisions
We build predictive models, recommendation engines, and data pipelines — demand forecasting, churn prediction, lead scoring, computer vision — that plug into your existing systems and turn historical data into decisions your team can act on.
Applied Machine Learning, Not Research Projects
Our ML practice is deliberately narrow: we build models that ship into production and get used, not research notebooks that never leave a Jupyter file. Every engagement starts with a clear business metric the model needs to move.
Problem framing that ties model output to a concrete business metric
Data audit and pipeline design before any model training begins
Classical ML (XGBoost, scikit-learn) preferred over deep learning where it performs as well and is cheaper to run
Model evaluation against a held-out test set and business-relevant baselines
Deployment as a REST API or batch pipeline integrated into your existing stack
Monitoring for model drift and scheduled retraining
Key Features
Demand & Sales Forecasting
Time-series models predicting inventory needs, staffing levels, or revenue, reducing overstock and stockouts.
Lead & Churn Scoring
Classification models ranking leads by conversion likelihood or flagging at-risk customers before they churn.
Recommendation Engines
Collaborative filtering and content-based recommenders for e-commerce, media, or content platforms.
Computer Vision
Image classification, object detection, and OCR pipelines for quality control, document processing, and inventory counting.
MLOps & Deployment
Model packaging, versioning, and deployment as scalable APIs with automated retraining pipelines.
Model Monitoring
Drift detection and performance dashboards so you know the moment a model's accuracy starts degrading in production.
Our Process
Problem Framing
1 weekDefine the business metric the model must improve and what a successful outcome looks like in production.
Data Audit
1-2 weeksAssess data quality, volume, and labelling gaps. Identify what data collection needs to happen before modelling starts.
Model Development
3-8 weeksIterative training and evaluation against baselines, with weekly checkpoints on accuracy and business impact.
Validation
1-2 weeksTest against held-out data and, where possible, a real-world shadow deployment before full rollout.
Production Deployment
1-2 weeksPackage the model as an API or batch job, integrate into your existing systems, and set up monitoring.
Monitoring & Retraining
OngoingTrack model drift and retrain on a schedule or trigger, so accuracy doesn't silently degrade over time.
Technology Stack
Modelling
Computer Vision
MLOps
Data Infrastructure
Pricing Options
ML Proof of Concept
Validate whether a model can hit the accuracy needed to justify production investment.
Production ML System
A trained model deployed as a live API or pipeline integrated into your existing systems.
Enterprise ML Platform
Multiple models, automated retraining pipelines, and enterprise-grade monitoring across teams.
Real-World Use Cases
Satellite imagery and sensor-based crop yield prediction model deployed across 500+ farms in Punjab.
Computer vision model extracting line items from scanned invoices for automated ERP entry.
Demand forecasting model for a multi-outlet retail chain, reducing overstock while preventing stockouts.
Symptom-checker classification model integrated into a telemedicine app's triage flow.
Frequently Asked Questions
Do we need a huge dataset to get started?
Not always — it depends on the problem. Classical ML models can work well with a few thousand labelled examples. We run a data audit first to tell you honestly whether your current data is enough, or what needs to be collected first.
Do you always use deep learning?
No. We default to classical ML (XGBoost, gradient boosting, linear models) unless the problem genuinely requires deep learning (e.g. computer vision, NLP). Classical models are cheaper to run, easier to explain, and often just as accurate for tabular business data.
What happens if the model's accuracy isn't good enough?
This is exactly why we start with a Proof of Concept phase — you get an honest accuracy benchmark before committing to full production deployment. If the numbers don't justify the investment, you'll know before spending on infrastructure.
How do you prevent model performance from degrading over time?
We set up drift monitoring that tracks prediction accuracy against real-world outcomes, with alerts and a scheduled or triggered retraining pipeline so the model stays accurate as your data changes.
Ready to get started?
Let's discuss your project and turn your ideas into reality.