AI Engineering

End-to-end ML systems, from architecture to production.

What we do

AI engineering is the discipline between research and deployment. We design machine learning systems that are not just accurate in the lab but reliable, maintainable, and observable in production. That means choosing the right architecture for the problem, building the data pipelines that feed it, and instrumenting the system so you know when it’s working and when it isn’t.

We work across the full ML lifecycle: problem framing, dataset design, model selection and training, evaluation, deployment, and monitoring. We’re comfortable with both classical ML and deep learning, and we’re opinionated about when each is appropriate.

Our engineering practice is shaped by our research work in physical AI. We’ve seen what happens when ML systems meet real-world conditions — sensor noise, distribution shift, adversarial inputs — and we build with those failure modes in mind from the start.

Industries served

Use cases

Where we apply this.

Model Architecture Design

Selecting and designing model architectures matched to problem constraints — latency, accuracy, compute budget, and data availability.

MLOps & Production Deployment

Building the infrastructure to deploy, monitor, and update ML models in production. CI/CD pipelines, model registries, and drift detection.

Data Pipeline Engineering

Designing data collection, labeling, and preprocessing pipelines that produce training data at the quality and scale the model needs.

Evaluation & Benchmarking

Rigorous evaluation frameworks that measure what matters in production, not just held-out test accuracy.

Model Optimization

Quantization, pruning, distillation, and compilation for deployment on constrained hardware without sacrificing critical performance.

LLM Integration

Integrating large language models into production workflows — RAG systems, fine-tuning, prompt engineering, and evaluation.