AI · AI Engineering
AI Evaluation & Monitoring
AI evaluation and monitoring is the engineering of the safety net under a live LLM or agent system: labelled evals that catch quality regressions before deploy, and production monitoring that flags drift, failures, and cost spikes before users do. We instrument retrieval, generation, and task success, wire evals into CI, and stand up the dashboards and alerts your team runs on. Senior engineers own the build.
In short
What is AI Evaluation & Monitoring?
AI evaluation and monitoring is engineering evals and observability for production LLM and agent systems. Metaborong builds a labelled evaluation harness that catches regressions in CI, and live monitoring for drift, failures, latency, and cost. Retrieval, generation, and task success are instrumented and alerted, so problems surface before users hit them. Senior engineers own the build, delivered from India.
What we deliver
Concrete artefacts, not capabilities
- 01
Labelled evaluation harness covering retrieval, generation, and task success
- 02
Evals wired into CI so regressions block deployment
- 03
Production monitoring for drift, failures, latency, and cost
- 04
Alerting and dashboards your on-call team runs on
- 05
Scorecards and a regression log that track quality over time
Key concepts
Key terms, defined
- LLM evaluation
- LLM evaluation is the scoring of a model or pipeline against a labelled dataset on metrics like accuracy, relevance, and task success, run automatically so quality is measured continuously rather than judged by spot-checking a handful of outputs.
- Drift
- Drift is the gradual divergence of a live system's inputs or outputs from what it was built and evaluated for. Undetected, it degrades quality silently. Monitoring flags drift so it is caught before users feel the effect.
- Observability
- Observability for AI systems is the instrumentation of latency, cost, error rate, and quality signals from production traffic, so a team can diagnose why a model's behaviour changed, not just see that a metric moved.
How we work
Engagement phases
Eval design
We define what good looks like for your system: the tasks that matter, the failure modes that hurt, and the metrics that capture them. A labelled dataset is built from real traffic and edge cases, so scores reflect production reality rather than a benchmark that flatters the model.
Harness and CI
The evaluation harness scores retrieval quality, generation quality, and end-to-end task success, and runs in CI on every change. A regression below threshold blocks the deploy. Scores are versioned, so a quality change is traceable to the commit and prompt that caused it.
Production monitoring
We instrument the live system: latency, error rate, token cost, and quality signals sampled from real traffic. Drift detection flags when inputs or outputs shift away from what the evals cover. Alerts route to your on-call channel with enough context to act, not just a number that moved.
Operations and handover
Dashboards and alerts are tuned to your thresholds and on-call rhythm, with runbooks for the common failure modes. Production incidents and edge cases feed back into the eval set, so the safety net tightens over time. We hand over so your team owns evals and monitoring without us in the loop.
Tech stack
What we build on
- OpenAIModels
- AnthropicModels
- LangfuseEval + tracing
- pytestCI harness
- GrafanaDashboards
- SentryErrors
- PostgreSQLScores
- DatadogMonitoring
- OpenAIModels
- AnthropicModels
- LangfuseEval + tracing
- pytestCI harness
- GrafanaDashboards
- SentryErrors
- PostgreSQLScores
- DatadogMonitoring
Scope
When this fits and when it doesn't
| This fits when | This doesn't fit when |
|---|---|
| You have an LLM or agent feature in production and cannot tell when it regresses. | You have not shipped an AI feature yet - evals come once there is something to measure. |
| You ship prompt or model changes and need a gate that catches quality drops. | You want a one-time audit with no production instrumentation - we build the live system. |
| You need cost, latency, and drift visibility your on-call team can act on. | You expect a generic monitoring install - we engineer evals specific to your tasks. |
Related services
Adjacent engagements
- AI
AI Agent Development
Custom autonomous and multi-agent systems that plan, use tools, and report, with evals and guardrails.
- AI
GenAI APIs & Backend Integration
Architect, integrate, and harden LLMs in your stack: auth, routing, fallback, cost controls, observability.
- AI
RAG & Retrieval Pipelines
Retrieval pipelines that ground LLMs in your data: embeddings, vector stores, reranking, evaluations.
Frequently asked questions
AI evaluation and monitoring is the safety net under a production LLM or agent system. Evaluation scores the system against a labelled dataset so quality regressions are caught in CI before deploy. Monitoring instruments the live system for drift, failures, latency, and cost, with alerts, so problems surface for your team before they reach users.
By the time users complain, the regression has already shipped and the damage is done. Evals catch quality drops before deploy, and monitoring flags drift and failures from sampled traffic within minutes, not after a support backlog forms. User feedback is valuable, but it is a lagging signal; instrumentation is the leading one.
Yes, that is the common case. We instrument the live system, build a labelled dataset from your real traffic and known edge cases, and wire evals into your CI without a rewrite. Existing features keep running while the safety net is added underneath them. Most of the work is measurement, not changing your model.
Retrieval quality, generation quality, and end-to-end task success for the evals; latency, error rate, token cost, and drift for monitoring. The exact metrics are designed around your tasks, because a generic dashboard misses the failures that matter to your product. Production incidents feed back into the eval set so coverage grows.
Last reviewed · Reviewed by Metaborong engineering team
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