Free Interactive Scorecard — 2 Minutes

How good is your AMD setup?

Answer 7 quick questions about how your outbound team detects answering machines today. Get an instant maturity score and see exactly where false positives and wasted talk time are creeping in. Nothing is sent anywhere — it runs entirely in your browser.

Quick answer

What makes answering machine detection accurate? The strongest AMD setups use transcript-based AI classification instead of beep detection, language-aware models for every market you call, a feedback loop where agent corrections improve detection over time, an agent override to rescue misclassified live humans, per-carrier tuning, measured false-positive rates, and automatic skipping of confirmed voicemails. This free scorecard rates your setup across those seven dimensions.

1 How does your current dialer detect answering machines?
2 Is your detection language-aware for the languages you call?
3 When an agent marks a detection as wrong, does your system learn from it?
4 If a live person is misclassified as a machine, can the agent rescue the call?
5 Is detection tuned for your specific carriers and regions?
6 Do you measure false-positive and false-negative rates?
7 How do you handle calls confirmed as voicemail?

Your AMD maturity score

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Answer the questions

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This is an illustrative self-assessment to help you reflect on your current setup — not a measurement of any specific product's accuracy and not a guarantee of results. AMD accuracy in production depends on audio quality, carrier behaviour, list quality, language mix and configuration. See our benchmark methodology for how we measure detection.

FAQ

Answering machine detection questions

What is a good AMD false-positive rate?
A false positive is a live person wrongly classified as a machine and dropped — the most costly AMD error because it discards a real conversation. Teams generally aim to keep false positives low while not letting voicemails through; the right balance depends on campaign type and how expensive a missed live contact is. The key is to measure it rather than guess.
Why does beep detection struggle across languages?
Beep and tone detection rely on acoustic signals that vary widely by carrier, accent and voicemail system, so they misfire on non-English calls and unusual greetings. Transcript-based classification reads the opening words instead, and meaning generalises across languages far better than beeps do — which is why language-aware models tend to be more robust in multilingual markets.
How can agent feedback improve AMD accuracy?
When an agent marks a detection as wrong, that correction can feed tenant-scoped tuning so the model adapts to your specific carriers, regions and campaign types over time. A static model that never sees agent feedback cannot improve, which is why a feedback loop is one of the strongest signals of a mature AMD setup.
Is this scorecard storing my answers?
No. The assessment runs entirely in your browser and nothing is submitted or stored. Your score and recommendations are computed locally as you answer.