MLOps
Models are software. Run them like it.
The pilot worked; production is a different sport. We build the operations layer — deployment, monitoring, evaluation, retraining — that keeps models and LLM applications honest after launch.
What we build
- Deployment pipelines for models and LLM apps
- Production monitoring: quality, drift, latency, and cost
- Evaluation as routine, not ceremony
- Update and retraining loops with human review where it matters
When teams come to us
- A model that works in a notebook nobody dares deploy
- Quality degrading silently until a customer notices
- AI costs that surprise finance every month
How we build it
Baseline and metrics
define what "good" means before touching anything
Deployment pipeline
models and LLM apps ship like software
Monitor what matters
quality, drift, latency, cost
Update loop
retraining and prompt changes with human review
Works well with
FAQ
We only use LLM APIs — do we still need this?
Yes: prompts, versions, evaluations, and costs degrade silently too.
Can you operate models our team built?
Common case: we instrument first, then improve.
What does running AI cost?
Depends on volume — the practice is making cost visible and reviewed like code, so the answer is never a surprise.