Your pipeline has 4 stages.
Which one is failing?
Real humans evaluate each stage of your pipeline independently, so you know which component to fix. You built a multi-stage AI system and when the output is wrong, you don't know which stage broke. Was it bad transcription? Irrelevant retrieval? A hallucinated conclusion? Without per-stage human eval, you're debugging a black box.
Every evaluation on Candor is completed by a real person. Not an LLM. Not a synthetic label. Human judgment.
You're optimizing blind
End-to-end testing hides per-stage failures
Your pipeline looks great on your 10 favorite test cases. But you have no idea if your retrieval is surfacing the right documents or if your LLM is reasoning correctly over the context it receives. A correct final output can mask a broken intermediate step.
You're two people. You can't evaluate everything yourselves.
You and your cofounder review outputs, but you're biased — you built the prompts, you know the expected answers, you unconsciously fill in gaps. You need strangers evaluating each stage to find the failures you can't see.
You don't know which stage to improve next
Should you fine-tune your transcription model, re-rank your retrieval, or rewrite your generation prompt? Without per-stage quality metrics, you're guessing where to spend your engineering time. One Candor study per stage tells you exactly where the ceiling is.
One study per stage. Fix the right one.
Is your perception stage accurate?
Your pipeline starts by converting raw input into structured data — audio to text, images to data, documents to fields. Have human evaluators rate the accuracy of this first stage in isolation. Catch errors before they cascade downstream.
“Rate the accuracy of this transcription compared to the original audio.”
Is your retrieval surfacing the right context?
Your system retrieves context from a document store before generating an answer. But is it pulling the right documents? Have human evaluators look at query-document pairs and categorize each retrieved result. Find out if your retrieval is the bottleneck before you blame the LLM.
“Is this retrieved document relevant to the query?”
Is your reasoning stage correct?
Your LLM receives context and produces a conclusion — a summary, a classification, a detected contradiction, a recommended action. Have human evaluators judge whether the conclusion is correct given the provided context. Pairwise comparison works well here: show two different model outputs and ask which is more accurate.
“Given the source context, which conclusion is more accurate? Explain your reasoning.”
Does the full pipeline actually help your user?
Once you've evaluated individual stages, test the complete experience. Put real domain experts — lawyers, doctors, analysts, whoever your actual users are — in front of the full pipeline output and run an AI-moderated voice session. The moderator probes on trust, usefulness, and moments of confusion.
Learn more about Voice Interviews →The eval stack for compound AI systems
Compound AI systems need compound evaluation. You wouldn't ship a traditional software product by only testing the final output — you test units, integration, and end-to-end. AI pipelines deserve the same discipline. Candor makes it possible to run per-stage human eval without building internal tooling for each step. One command per stage. Results in hours, not weeks.
Find the weak link in your pipeline
Fix the right stage. Ship with confidence.