Trace capture
Collect inputs, model outputs, retrieved context, tool calls, errors, latency, and cost for each representative workflow.
LLM evaluation platform
Evaluate LLM behavior before production users find the failure.
A production LLM evaluation platform should compare model, prompt, retrieval, and tool changes against the workflows users actually run. Vectory focuses on traces, evidence, stop conditions, and release decisions.
Direct answer
An LLM evaluation platform should score final answers and the trace that produced them. The trace shows whether the system retrieved useful evidence, used tools with discipline, recovered after failures, and verified completion.
Deterministic checks
Enforce required observations, source use, tool budgets, approval gates, stop conditions, and completion verification.
Reviewer workflow
Route high-judgment failures to calibrated human or LLM-as-judge review with stable rubrics.
Evaluation guides
Go deeper on what to evaluate
- RAG evaluation
Evaluate retrieval quality, evidence diversity, faithfulness, and answer grounding before changing models.
- Agent evaluation platform
Evaluate AI agents with trace analysis, tool-use checks, recovery scoring, and completion verification.