A model that works in a notebook is maybe a third of the job. The rest is getting it deployed, watched and retrained without someone doing it by hand at midnight.
Tell us what you want to build. A senior engineer sends back a scope and a fixed price within two business days, no obligation.
In short
What we build
Automated training, testing and deployment, so releasing a model is a routine step and not a project.
Alerts for data drift, falling accuracy, latency and cost, aimed at the people who can act on them.
Prompt and model versioning, evaluation on every change, request tracing and spend control for LLM apps.
GPU serving for open models, autoscaling, and on-premises setups when the cloud is not an option.
Fit
Process
How models get trained, deployed and checked today, and where it hurts.
Often monitoring, because silent failure is the expensive kind.
Using tools your team can run: MLflow, Kubernetes, your cloud's native services.
Runbooks and training so the setup outlives the engagement.
Pitfalls
We have seen these enough times to plan around them from the start.
Next step
Want to see similar work? Browse our case studies or tell us what you are working on.
FAQ
MLOps covers training, deploying and monitoring models. LLMOps applies the same thinking to language model apps, adding prompt versioning, evaluation sets and token cost tracking.
Whatever fits your stack. Often MLflow, Docker and Kubernetes, plus the native ML services on AWS, Azure or Google Cloud.
Yes. Most engagements pair our engineers with your data scientists so the setup matches how they actually work.
Yes. We set up on-premises and private cloud serving, including GPU hosting for open models.
Keep reading
Indicative ranges only get you so far. Tell us the specifics and get a scope and a fixed price in two business days.