MLOps & AI Infrastructure

Models in notebooks don't generate revenue.

Getting a model to work in a Jupyter notebook is the easy part. Getting it to serve 10,000 requests a day, retrain automatically when data drifts, and alert you before it starts degrading — that's the infrastructure problem most AI teams underestimate. We build that infrastructure.

Start a project

What we deliver

Training and fine-tuning pipelines (PyTorch, HuggingFace)

Model serving infrastructure (vLLM, TGI, Triton)

Feature stores and data pipeline design

Model monitoring, drift detection, alerting

GPU cost optimization and right-sizing

CI/CD for ML models (automated retraining, evaluation)

Vector database design and scaling

A/B testing frameworks for model evaluation

Common problems I solve

GPU costs are out of control

We audit your inference stack and right-size it. In most cases, we find 30–50% cost reduction through batching, quantization, and serving engine selection — without a quality drop.

Model performance degrades silently in production

We build monitoring pipelines that track output quality, latency, and data drift. You get alerts before users notice something is wrong.

Retraining is a manual process

We design automated retraining pipelines triggered by data volume, drift metrics, or a schedule — with automated evaluation gates before any model goes to production.

AI in production causing problems?

Describe the situation. We'll tell you what's fixable and how fast.

Get in touch