AI That Actually Works in Production
Most AI projects fail between prototype and production. The model works in a notebook, then breaks on real data, at real scale, with real latency requirements. We have navigated this transition 25+ times. Our AI engineering practice is built around production reliability first, accuracy second.
LLM Engineering
We build LLM-powered features that are reliable, cost-controlled, and observable. RAG pipelines with proper chunking, embedding, and retrieval strategies. Prompt engineering with version control. Structured output parsing. Cost monitoring and model routing for optimal price/performance across OpenAI, Anthropic, Google, and open-source alternatives.
Machine Learning
- Custom model training with PyTorch and TensorFlow
- Fine-tuning foundation models for domain-specific tasks
- Computer vision — object detection, segmentation, OCR
- NLP — classification, NER, sentiment analysis, summarisation
- Time-series forecasting for demand planning and anomaly detection
- Recommendation engines for personalisation at scale
MLOps Infrastructure
Model training pipelines with DVC for data versioning. Experiment tracking with MLflow. Automated retraining on data drift. A/B testing for model versions. Triton Inference Server for high-throughput GPU serving. Monitoring for data drift, model performance, and prediction quality.