Evaluate AI changes against evidence your team can inspect.
AI behavior needs the same operational discipline as production services. CloudGrid keeps datasets, evaluations, comparisons, optimization runs, metrics, and trace-backed evidence inside the same project where the rest of the system is observed.
AI quality reviews need production context, not isolated screenshots.
A CEO or CTO needs to know whether a change is ready, why it is better, and what evidence supports the decision. CloudGrid gives AI teams a repeatable evaluation loop while keeping source traces, runtime metrics, expected outputs, and observed results reviewable together.
From example to promotion decision.
Evaluation and optimization are not one feature. They are the path from "we know what good should look like" to "we can explain why this candidate is ready." CloudGrid keeps every step inside the project that owns the telemetry evidence.
Datasets make expected behavior explicit: inputs, expected outputs, reasons, splits, curation state, and optional source evidence.
Evaluations execute a target against a dataset split and produce aggregate metrics plus row-level expected versus actual results.
Baseline and candidate runs make improvements, regressions, latency changes, and representative examples visible before promotion.
Quick-shot optimization can explore candidates, but promotion remains explicit and should be backed by validation evidence.
What CloudGrid provides
- Schema-backed datasets for classification, extraction, generation, and workflow checks.
- Rows with input, expected output, optional observed output, reason, split, curation status, and source evidence.
- JSON validation against the dataset schema before rows become ready.
- Dataset evaluations against a target using project model aliases and metric defaults.
- Per-row expected vs actual output, metric results, trajectory summaries, important steps, and trace links.
- Baseline and candidate comparisons with improvements, regressions, latency tradeoffs, and representative examples.
- Quick-shot optimization for candidate exploration followed by explicit validation before promotion.
Operating model
- The primary v1 workflow is dataset evaluation and optimization.
- Production measurement is on the product path after the core evaluation loop.
- Complex agents and workflows can be evaluated through an adapter endpoint.
- Provider API keys belong in project or company provider settings and stay out of datasets and rows.
- Evaluation produces metrics, results, and comparisons that downstream alerting or release gates can consume later.
Different ways to build useful evaluation evidence.
Enterprises usually need more than one input path. Some examples are curated deliberately; others come from production traces; complex workflows may stay outside CloudGrid and expose an evaluation target through an adapter.
Teams encode known customer messages, extraction cases, workflow checks, or generation tasks as reusable dataset rows.
Production traces can become source material when a real workflow exposes a missed classification, missing field, or wrong tool path.
Complex agents and workflows can be evaluated through an adapter endpoint instead of being rebuilt inside CloudGrid.
Latency, model calls, token totals, and cost signals can sit beside evaluation results when they are emitted as OTLP metrics.
Production examples can become evaluation evidence.
Many useful evaluation rows start as production evidence: an unexpected category, a missing JSON field, a tool call that chose the wrong path, or a workflow that took too many steps. CloudGrid keeps the source trace link next to the dataset row and the evaluation result, so debugging and optimization stay connected.
AI changes can be reviewed as business decisions with evidence, not only as engineering experiments.
Teams get a repeatable path from production example to dataset, evaluation, comparison, optimization, and promotion.
Expected outputs, observed outputs, metrics, traces, and optimization candidates stay connected in one project.
