Artificial Intelligence & Machine Learning
Models that drive real decisions — forecasting, classification, and generative systems — engineered from data to production with monitoring and governance.
Most AI effort dies in the gap between a promising notebook and a system a business can rely on. We close that gap. We frame the decision a model is meant to improve, build against a clear definition of “good,” and put it into production with the monitoring, retraining, and governance that keep it trustworthy over time. Where large language models help, we apply them deliberately — behind guardrails, not hype.
A few of these usually point here.
- 01You have models in notebooks but none influencing real decisions.
- 02Data science and engineering keep re-solving the same deployment problems.
- 03A shipped model's accuracy is drifting and nobody is watching.
What we do
Predictive & classification models
Forecasting, churn, risk, demand, and anomaly detection built against real decisions and validated on held-out data.
Generative & LLM systems
Retrieval-augmented assistants and content systems grounded in your data, with evaluation and safety built in.
MLOps & lifecycle
Reproducible pipelines, a model registry, and drift monitoring so models stay dependable after launch.
Responsible AI
Bias checks, explainability, and human-in-the-loop review for models that touch high-stakes outcomes.
- Models in production against real decisions — not notebooks on a shelf
- Drift and performance monitored, with a clear retraining path
- Portable by design, so you are never locked to one cloud’s ML service
We start from the decision and its success metric, prove value in shadow mode against real data, and only then promote to production — with monitoring and governance from day one.
Concrete artifacts you own.
- Validated models deployed against real decisions.
- Reproducible training and serving pipelines with a model registry.
- Drift and performance monitoring with alerts and a retraining path.
- Evaluation and guardrails for any LLM-based system.
Good questions.
Can you work with our existing data platform?
Yes. We build on the data foundations you already have and add only what production ML needs — pipelines, a registry, and monitoring.
How do you handle LLMs and hallucination?
We ground generative systems in your data with retrieval, add automated evaluation, and gate outputs — so quality is measured, not assumed.