Your agent talks to your customers.
Do you know how it sounds?
Put your agent's outputs in front of real people — not another LLM — and get actual signal on tone, trust, and clarity. Your AI is representing your brand in every interaction — writing emails to leads, responding to support tickets, answering phone calls. You're probably evaluating it by reading logs and hoping for the best.
Every evaluation on Candor is completed by a real person. Not an LLM. Not a synthetic label. Human judgment.
You're flying blind on the thing that matters most
You've never tested this with real people
You read the logs, they look fine to you — but you built the thing. You have no idea how a stranger perceives the tone, clarity, or trustworthiness of what your agent writes.
Your feedback loop is lagging
You find out an agent response was bad when a customer churns, a lead ghosts, or a support ticket escalates. By then you've sent that same bad response to hundreds of people.
Vibes don't scale
You and your cofounder read through outputs and gut-check them. That works at 10 conversations a day. It doesn't work at 10,000.
Three ways to eval your agent with real people
Which response variant converts?
Your agent can respond to an inbound lead five different ways. Which one makes a real person most likely to reply, book, or buy? Run a pairwise comparison across response variants with real people who match your customer profile. Get a ranked leaderboard with win rates.
“Which of these two responses would make you more likely to book an appointment?”
Does your agent sound trustworthy?
Have real people rate your agent's outputs on trust, professionalism, clarity, and helpfulness. Get per-dimension scores with standard deviations so you know where you're strong and where the AI sounds off.
“Rate this response on trust, professionalism, clarity, and helpfulness.”
Watch real users interact with your agent
Put real people in front of your agent experience. An AI voice moderator observes and asks follow-up questions as they interact — probing on moments of confusion, trust breakdown, or friction. Get transcripts and themes, not just satisfaction scores.
Learn more about Voice Interviews →Agent companies have a unique eval problem
Traditional A/B testing requires massive traffic and measures lagging indicators — conversion, churn, escalation rates. Human eval gives you leading indicators: you know a response sounds wrong before it costs you a customer. For agent companies where every output is a customer touchpoint, this isn't nice-to-have research — it's QA.
Run your first agent eval
Describe what you want to learn. Candor handles the rest.