AI & Machine Learning

AI that works in production

We build machine learning systems that deliver value - not impressive demos that fall apart when you try to deploy them.

What we build

Text classification, sentiment analysis, entity extraction, document understanding. Processing unstructured text at scale.

Image classification, object detection, OCR, visual inspection systems. From quality control on manufacturing lines to document processing.

Recommendation engines that learn from user behavior. Product recommendations, content suggestions, dynamic pricing.

Forecasting, anomaly detection, risk scoring. Models that help you anticipate problems before they happen.

Our approach

Not every problem needs AI. We start by understanding what you're trying to achieve and whether machine learning is actually the right approach. Sometimes a rules engine is better.

ML models are only as good as their training data. We evaluate what data you have, what you need, and how to handle gaps before writing any code.

We build simple models first, validate they work, then increase complexity if needed. You see results early and can course-correct.

A model isn't useful until it's deployed. We handle the engineering to make ML work reliably in production - monitoring, retraining pipelines, drift detection.

Technical stack

Frameworks: PyTorch, TensorFlow, scikit-learn, Hugging Face
MLOps: MLflow, Kubeflow, custom deployment pipelines
Infrastructure: GPU clusters, AWS SageMaker, Google Vertex AI
LLMs: Fine-tuning, RAG systems, prompt engineering

Use cases we've shipped

Customer service chatbots and ticket classification. Invoice extraction and contract analysis. Visual inspection for manufacturing defects. Product recommendations for e-commerce. Demand forecasting and inventory optimization.

What makes us different

We're pragmatic about AI. We won't recommend deep learning when logistic regression will do. We focus on solutions that are maintainable, explainable, and actually move your business metrics.