Applied ML | Custom Models | Research to Production
Machine Learning Research & App Development
Machine learning works best when it's built around your specific data and goals. We don't just plug in off-the-shelf models — we research, experiment, and engineer solutions that outperform generic approaches.
Our ML team bridges the gap between research and real-world deployment, delivering models that are accurate, interpretable, and production-ready.
Talk to UsWhat We Offer
Custom Model Development
End-to-end ML pipelines tailored to your data (tabular, text, image, time series).
Foundation Model Fine-Tuning
Fine-tune LLMs (GPT, LLaMA, Mistral) and vision models on your domain data.
Computer Vision
Object detection, image classification, OCR, document intelligence.
NLP & Text Analytics
Sentiment analysis, entity extraction, document summarisation, classification.
Forecasting & Predictive Analytics
Demand forecasting, churn prediction, anomaly detection.
MLOps & Model Deployment
Scalable model serving with FastAPI, TorchServe, or AWS SageMaker.
ML Research Consulting
Literature review, hypothesis design, benchmark experiments, and reporting.
Tech Stack
Use Cases by Industry
Healthcare
Diagnostic support models, medical image analysis
FinTech
Credit scoring, fraud detection, risk modelling
E-commerce
Recommendation systems, visual search
Startups
MVP ML features, dataset curation, model benchmarking
Manufacturing
Defect detection, predictive maintenance
Research to Production Process
Discovery
Define ML problem, data audit, and feasibility assessment.
Prototype
Experiments, baselines, and proof-of-concept models.
Build
Model training, fine-tuning, and rigorous evaluation.
Deploy
MLOps pipeline, API integration, and production monitoring.
Case Study
PyTorch | Scikit-learn | AWS SageMaker | MLflow
Custom churn prediction model for a FinTech startup
Built and deployed a customer churn prediction model with 87% accuracy, integrated into the product dashboard with real-time scoring via REST API.
Research-first approach
We start with experiments, not assumptions — every model is benchmarked before it ships.
Open-source + proprietary mix
We recommend the right model based on your cost, privacy, and performance needs.
Transparent reporting
Regular experiment logs, model cards, and performance reports at every milestone.