Designed to catch quality regressions before they hit production.
Eval pipelines, online monitoring, and CI gates—so every deployment is measured, safe, and reversible.
Overview
We build the operational backbone for your AI systems: eval harnesses that help prevent regressions, monitoring that surfaces drift before users notice, and deployment pipelines that make updates safe and reversible.
Who this is for
Ideal buyer / team
ML platform teams, AI engineering leads, or quality/risk owners
Best-fit workflow
AI system deployment, monitoring, and continuous quality measurement
Typical environment
Organizations with deployed AI systems that lack structured evaluation, drift detection, or deployment safety
Strong fit when…
When you need to demonstrate AI quality to regulators or leadership, or model updates are breaking production without warning
When to use this
- You've deployed AI but can't measure if it's getting better or worse
- Model updates break production without warning
- You need to demonstrate AI quality to regulators or leadership
- Your team spends more time debugging AI issues than building features
What you get
Reference architecture
LLMOps pipeline with eval harness, CI gates, and online monitoring
- Golden dataset management and versioning
- Offline eval runner (groundedness, task success, safety)
- CI/CD gate — deploy blocked if metrics regress past threshold
- Online monitoring layer (p95 latency, drift score, cost per success)
- Alert routing + incident workflow integration
- Rollback automation triggered by metric breach
- Executive dashboard with weekly trend digest
Typical timeline
Audit existing AI systems, define eval criteria, build golden datasets with SMEs.
Build eval harness, wire CI regression gates, and instrument telemetry.
Online monitoring, drift detection, alerting, and incident workflow.
Executive dashboard delivery, team training, and runbook documentation.
Success metrics
- Groundedness — % of claims traceable to source documents
- Task success rate — end-to-end completion on target workflows
- p95 latency — 95th-percentile response time
- Cost per success — total spend ÷ successful completions
- Drift score — statistical divergence from baseline distribution
Security & governance included
- Secure eval data storage
- Role-based dashboard access
- Audit log for all model changes
- Encrypted telemetry pipeline
- PII-free monitoring data
Integration-first
We connect to your stack
Frequently asked questions
Want to talk through your use case?
Book a 30-minute discovery call. We'll scope the workflow, map integrations, and outline security controls—no commitment required.