Node AI.08
AI evaluation, guardrails and LLMOps
Know your AI works before customers find out it does not.
Evaluation suites, safety guardrails, monitoring, cost control and observability for LLM applications and AI agents running in production.
The problem
AI systems change behaviour when a prompt is edited, a document is added or a model version is updated. Without tests, nobody knows whether this week is better or worse than last week.
Costs drift upward, latency spikes go unnoticed, and the first sign of a bad answer is a customer screenshot. Production AI needs the same discipline as any other production system.
What we build
- Evaluation sets built from real usage, with automated scoring for accuracy, groundedness and tone
- Regression tests that run on every prompt, model or data change
- Guardrails for prompt injection, data leakage, off-topic requests and unsafe content
- Tracing and dashboards for latency, error rates, token usage and cost per conversation or task
- Budget alerts and model routing to keep spend predictable
How it works
Every request is traced. Guardrails check inputs and outputs. Samples feed the evaluation set, which runs automatically before each change ships. Dashboards and alerts track quality and cost.
- Step 1InstrumentAdd tracing to every model call, retrieval and tool call.
- Step 2Build the eval setCollect real inputs, label expected behaviour and define scoring.
- Step 3Add guardrailsInput and output checks for the risks that matter in your context.
- Step 4Gate releasesChanges ship only if the evaluation scores hold.
- Step 5Watch productionDashboards and alerts for quality, latency and cost.
Deliverables
- Evaluation suite and baseline scores
- Guardrail configuration
- Tracing and monitoring dashboards
- Release checklist and CI integration
- Cost report with recommendations
Typical stack
- Python
- LLM and agent frameworks
- Google Cloud
We choose tools to fit your environment; this is where we usually start.
Ways to work with us
Questions
We already have an AI app in production. Can you add this?
Yes. Most of this work is added around an existing system: tracing, an evaluation set from its real traffic, and guardrails, without rewriting the app.
How do you score answers automatically?
With a mix of exact checks, retrieval checks and model-graded rubrics, calibrated against a sample that people have labelled.
Can this reduce our AI costs?
Often. Tracing shows where tokens go; caching, shorter prompts and routing simple requests to smaller models are the usual savings.
Talk to us about aI evaluation, guardrails and LLMOps for your business.
Send a short brief. We reply by email, usually with questions before we propose anything.