Machine Learning & AI Integration.
Models in production, not notebooks.
What we offer
We take models from a Jupyter notebook to a monitored, versioned, rollback-able service with real users on the other end.
A model that isn’t monitored is a model that has already drifted.
Evals are the unit test of ML — we build them before the model.
LLM features need guardrails, not just prompts. We ship both.
RAG & LLM Pipelines
Retrieval-augmented generation with vector stores, evals, and guardrails for production use.
MLOps & Model Serving
Versioned models, canary rollouts, drift detection, and feature stores with Feast.
Data Engineering
Batch and streaming pipelines with Airflow, dbt, and Spark on managed clusters.
Evaluation & Observability
Offline evals, online A/B, and LLM tracing so you know what your model actually does.
The shape of a machine platform
Hover a layer to inspect it
Tools we reach for
Rebuilding Search Relevance with RAG
A retrieval-augmented search system lifted conversion 28% and cut “no results” pages by 90%.
Read the case study