MLOps, LLMOps, and FinOps
Infrastructure to run models and agents in production: monitoring, continuous evaluations, cost control, and smart routing between models.
AI That Works in Production Today, Tomorrow, and With Costs Under Control
The challenge
Getting a model into production is the beginning, not the end. Models change, data changes, and costs can grow out of control.
Our answer
We design the infrastructure that keeps your AI systems accurate, secure, and efficient over time: continuous evaluations, monitoring, and cost control.
- Continuous evaluation
- Observability
- Costs under control
How we start
- 01Automated evaluations on every change
- 02Monitoring of quality, latency, and cost
- 03Ongoing optimization and scaling
What it looks like in your industry
Situation
A risk model loses accuracy over time and no one notices in time.
What we do
- We set up performance monitoring and degradation alerts.
- We automate retraining and comparison with the current version.
Result
Each new version ships only if it beats the previous one.
Situation
The store's assistant uses the most expensive model for every query.
What we do
- We route each query to the right model based on its complexity.
- We measure token usage and latency in real time.
Result
You pay only for the capacity each query needs.
Situation
Patient data can't leave for an external provider.
What we do
- We deploy open-weight models in your own cloud.
- We mask personal data with guardrails.
Result
AI in production with sovereignty over your data.
Illustrative example
Our process
Automated Evaluations
Accuracy, hallucination, and regression tests that run on every change before it reaches production.
Model Routing
Each request goes to the best-fit model based on complexity and cost, so you only pay for the capability you need.
Observability and Traceability
Real-time monitoring of latency, token usage, prompt history, and user feedback.
Deployment and Scaling
Infrastructure in the cloud or on your own servers with containers, CI/CD, and auto-scaling.
Private Models
Deployment of open-weight models in your own cloud when privacy or data sovereignty requires it.
Guardrails and Data Protection
Security layers against attacks and masking of personal data before it reaches an external provider.
What we've achieved
At *Validate Health* we took patient cost models to production on Databricks with MLflow, Unity Catalog, and Model Serving, raising reliability from 50% to 87.8%, and at *Parallel Analytics*, *BTH*, and *Semtel* we have taken data and agents to production and kept them running. That operational experience is what allows us to design AI infrastructure that is reliable, measurable, and cost-efficient.
