Every thumbs down makes it sharper.
Your team rates every AI reply with one tap. A thumbs down asks for the reason. Those lessons feed straight back into the agent — so the mistake you flagged today is the answer it gets right tomorrow.
Your AI is making the same mistake. Again.
Without a feedback loop, every AI reply is a one-shot. The bad ones get noticed. The cause never gets fixed. The same wrong answer ships next week.
Three steps. One closed loop.
Read
Your team reads AI replies in the inbox as they happen — same place they're already working. The thumbs sit right under every AI message.
Rate
One tap for thumbs up. Two taps for thumbs down — the second picks the reason from a fixed list (inaccurate, wrong tone, off-topic, missing detail).
Refine
Trends show up in the dashboard the moment your team rates a reply. Patterns become obvious in days, not quarters — so your team knows exactly which Knowledge Hub answers or Learned Q&As to update next.
The fastest signal an AI agent can possibly get.
Your team is already reading every AI reply. With one tap, that reading becomes training data.
One tap, in the conversation
Thumbs up and thumbs down sit directly under every AI message in the inbox. No separate review queue, no spreadsheets, no “remind me tomorrow.” React as you read — same screen, same flow.
Reason required on every miss
A thumbs down opens the “why?” picker. Four categories — the same four every time — so the signal stays clean and the trends become readable.
Refinement that compounds
Every rating sharpens the signal in real time. Week one’s 10 corrections become week two’s 6, become week ten’s 1 — as your team turns flagged replies into Knowledge Hub edits and Learned Q&As, the agent stops making the same mistake twice.
Trend dashboard
See which questions get flagged most. Which failure category dominates. Which agent is improving fastest. Turn flagged replies into a roadmap for the Knowledge Hub.
Per-agent feedback
Your sales agent and your support agent learn separately. A thumbs-down on a refund reply only refines the agent that sent it — not the unrelated booking bot in the next branch.
Operator-only, by design
Customers never see the thumbs. Feedback is internal — your team’s, your agents’, your data. The customer just gets a reply that quietly gets better.
Teams who’d rather improve the AI than babysit it.
Teams scaling AI replies
Going from 50 AI replies a week to 5,000? You can't audit them all manually. One-tap feedback turns every operator's reading into training signal — at the scale the AI is operating.
Compliance-conscious teams
Healthcare, finance, legal. Every flagged reply is captured with the reason, the operator, and the timestamp. Compliance reviews become a database query, not a Slack thread.
Multi-agent operations
Different agents for sales, support, bookings. Feedback stays scoped — each agent learns from its own flags. So your sales agent doesn't accidentally inherit your support agent's tone issues.
Continuous-improvement cultures
If you run weekly QA on your AI replies anyway, the loop just turns that ritual into a structured signal. Less effort, more data, faster improvement.
Questions you’re probably asking.
What teams want to know before turning their inbox into AI training data.
The signal lands in real time — every rating shows up in the trend dashboard within minutes. From there, your team acts: update a Knowledge Hub answer, promote a Learned Q&A, or refine the agent's instructions, and the next reply ships the fix. No prompt-engineering required, no maintenance tickets to file, no one else to wait on. The platform turns your team's ratings into the steering wheel — your team drives.
Still have a question?
Ask us on WhatsApp →Today’s mistake.
Tomorrow’s correct answer.
We’ll wire the loop into your inbox, walk your team through the four reason categories, and show you the first week of flagged replies — in your onboarding call.
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