AI Monitoring at scale
Architecture
We sample a statistically significant subset of your AI output.
We design evaluation criteria specific to your workflow's failure modes - not generic accuracy metrics, but the specific errors that matter in your context.
We apply LLM judges that assess output quality continuously, at scale.
Understand your quality levers, get notified when quality dips below a defined threshold, and prevent agents taking action when uncertainty is high.
This is the visibility you don't currently have.
Deliverables
Eval Pipeline
A sampling and evaluation pipeline deployed alongside your code.
Accuracy Dashboard
Track quality across dimensions specific to your business.
Alerting Integration
Slack, email, PagerDuty and others..
Ongoing Accuracy Report
Delivered to the surfaces you use.
Everything we build is yours. Code, data, dashboards, all of it.
Engagement Models
Fixed-Scope Evaluation Build
We assess your AI system, define the monitoring architecture, and build the evaluation pipeline.
Ongoing Monitoring Retainer
We build and operate the monitoring layer: reports, alerts, and continuous improvement of evaluation criteria.