MLOps · AI Ops
The specialty: MLOps and AI Ops
Most AI projects don't fail at the model. They fail at everything after it: the data that shifts, the deployment nobody can reproduce, the cost nobody is watching.
Notebook to production
Models that run outside someone's laptop: packaged, versioned, and deployable by anyone on the team.
Reproducible pipelines
Automated training, evaluation and deployment, with data and code versioned together so a result can be reproduced six months later.
Observability and drift
Data-quality and model-drift monitoring, with alerts that arrive before the customer complaint does.
Cost under control
Inference measured and budgeted. Local models where they make sense, APIs where they're worth paying for.
How we work
1. Diagnosis
One call and a review of what you already have. You leave with a written scope and a price range, not a twenty-page proposal.
2. Something shipped in weeks
Working software in your hands early, even if it's one slice. Projects that take months to show anything are the ones that get cancelled.
3. Measure, then continue
We instrument what we ship. If the number doesn't improve, we change the plan instead of defending it.