AI Run, Ops & governance: AI in production, supervised, under control and compliant.
Going live with AI is just the beginning. Keeping it reliable, predictable and auditable over time is our craft: continuous supervision, inference cost control, guardrails and evaluations that hold up in front of a regulator.
The problem
A shipped AI is not an AI under control
Once in production, models drift, token costs spiral without anyone knowing why, and unexpected behaviours go unnoticed until an incident hits. Without supervision or guardrails, every change from a provider or in your data becomes a silent risk — to quality, security and budget. In regulated environments, the absence of logs, evals and audit trails is simply a deal-breaker.
The decisive question is no longer “does it work?” but “will it stay reliable, predictable and demonstrable tomorrow, and at what cost?”
Our trust layer in 4 pillars
Supervision & SLA
Continuous monitoring of models and pipelines, alerting and on-call cover under a contractual SLA, 24/7 or during business hours.
Drift detection
Monitoring of quality and drift (inputs, outputs, distributions) so you can react before the incident, not after.
Guardrails & evals
Guardrails on prompts and outputs, continuous eval sets and regression tests with every model update.
Costs & audit
Control of token and inference costs, complete logs and audit trails ready for compliance and the AI Act.
Deliverables
What your AI run includes
- Continuous supervision, 24/7 or during business hours, with SLA and on-call cover
- Cost & quality dashboards: tokens, inference, latency, error rate
- A documented guardrails policy (input / output guardrails)
- Eval sets and regression tests replayed with every update
- Audit reports and traceability trails aligned with the AI Act
Industrialising AI, backed by the data & compliance DNA of Datanaos
Frequently asked questions
What does your supervision SLA cover?
The SLA defines the monitored scope, response times and targeted availability. We offer a 24/7 run with on-call cover or a business-hours run, depending on the criticality of your use cases and your exposure to risk.
How do you detect model drift?
We instrument inputs, outputs and quality metrics, then replay reference eval sets. Any significant variation triggers an alert and an investigation before it affects users.
How do you control token and inference costs?
Dashboards track consumption in real time by use case. We tune models, caching, context size and routing to cut the bill without degrading quality, with budget alert thresholds.
Are you compatible with a regulated environment and the AI Act?
Yes. Compliance is in Datanaos’ DNA: complete logs, guardrails, audit trails and documented reports make up the trust layer a regulator expects and align your AI with the requirements of the AI Act.
Move from experimentation to AI in production
Start with a short, fixed-price assessment: maturity, high-ROI use cases, and a prioritised roadmap. No commitment.