What We Build
Predictive Analytics
Demand forecasting, churn prediction, fraud detection, and risk scoring models deployed in your existing systems.
Recommendation Engines
Collaborative filtering, content-based, and hybrid recommendation systems for e-commerce, media, and SaaS products.
Computer Vision
Image classification, object detection, document processing, and visual inspection systems for manufacturing and healthcare.
Natural Language Processing
Sentiment analysis, entity extraction, text classification, document understanding, and conversational systems.
Our ML Stack
Frameworks
TensorFlow, PyTorch, scikit-learn, XGBoost, Hugging Face Transformers
Infrastructure
AWS SageMaker, GCP Vertex AI, Azure ML, custom on-premise deployments
Data Engineering
Apache Spark, dbt, Airflow, Kafka for ML data pipelines
MLOps
MLflow, DVC, Weights & Biases for experiment tracking, versioning, and monitoring
How We Work
Data Assessment
We audit your existing data, identify gaps, and define what's actually feasible before committing to a model.
Rapid Prototyping
A working prototype in 2–4 weeksso you see real results before full investment.
Production Deployment
We don't stop at notebooks. Models are packaged as APIs, integrated into your systems, and monitored in production.
Model Monitoring
Drift detection, performance dashboards, and retraining pipelines so your model stays accurate over time.