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How Voicemail Detection Works

Answering Machine Detection (AMD) enables your AI agents to automatically detect when outbound calls reach voicemail instead of a live person. When the agent detects voicemail, it hangs up and logs the outcome, allowing you to retry at different times to maximize chances of reaching a live person. This critical feature prevents wasted agent time talking to answering machines and optimizes campaign efficiency by focusing resources on live conversations rather than voicemail systems.
Phone Calls Only: Answering Machine Detection (AMD) applies to phone calls via Session Initiation Protocol (SIP)/Public Switched Telephone Network (PSTN) connections. Web-based conversations without a phone leg do not support voicemail detection.Access AMD configuration during campaign creation, campaign settings, and phone test calls. Text-based is the default mode. ML-based enables the external AMD participant for faster detection (~1.5s vs 5-15s).

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What is Answering Machine Detection?

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The Challenge

When making outbound calls, you encounter two possible scenarios: Scenario 1: Live Answer 
Phone rings → Person answers → "Hello?"
→ Agent should engage in conversation
→ Full agent capabilities needed
Scenario 2: Voicemail 
Phone rings → Voicemail system answers → "You've reached John Smith..."
→ Agent should hang up and retry later
→ Don't waste time with full conversation script
→ Don't create awkward interaction talking over voicemail greeting
The problem: How does the agent know which scenario occurred?

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AMD Solution

AMD analyzes the audio in the first few seconds after call connection to determine if a human or machine answered: Detection process: 
1. Call connects
2. AMD analyzes audio (0.5-3 seconds depending on method)
3. Classification: HUMAN or MACHINE
4. Agent executes appropriate behavior
Benefits:
  • Efficiency: Don’t waste agent time on voicemail
  • Better targeting: Focus retry attempts on different times to reach live person
  • Higher connect rates: Optimize call timing based on voicemail patterns
  • Better analytics: Separate “reached voicemail” from “no answer” in reporting

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AMD Methods

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Detection Types

Text-Based AMD

Detect voicemail using keyword analysis with your transcriber and LLM — the default mode

ML-Based AMD (Optional)

Recognize voicemail patterns using a Deep Neural Network — optional addon for faster detection

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Text-Based AMD

Role: Default detection mode. Uses your agent’s transcriber and LLM to identify voicemail keywords. How it works: 
1. Call connects
2. Agent's AI voice pipeline (transcriber) receives audio
3. Agent waits for first turn/utterance to complete (pause detected)
4. LLM analyzes transcription for voicemail-like patterns:
   - "You've reached"
   - "Leave a message"
   - "Not available"
   - "Voicemail"
   - "After the beep"
5. If voicemail detected → Agent hangs up
6. If live person detected → Agent continues normal conversation
Characteristics:
Slower: Waits for complete turn/utterance to finishMust wait for the entire voicemail greeting to complete (pause detected), then transcribe and analyze the full text. Typical detection occurs after 5-15+ seconds depending on length of voicemail message.Limitation: Long voicemail greetings mean longer wait times before detectionBest for: Campaigns where accuracy is more important than immediate detection
High accuracy when:
  • Standard voicemail greetings with common phrases
  • Clear audio quality
  • Voicemail language matches transcriber language
  • B2B environments with professional greetings
Will NOT detect:
  • Voicemail greetings in languages the transcriber doesn’t support
  • Voicemail systems with no greeting (just beeps)
  • Non-verbal voicemail indicators
Lower accuracy when:
  • Custom greetings without standard keywords
  • Short greetings (“Hi, leave a message” - very brief)
  • Poor audio quality or background noise
  • Background noise interfering with transcription
Ideal for:
  • B2B campaigns - Business voicemails typically use standard phrasing
  • Campaigns prioritizing accuracy - Reduces false positives by analyzing full utterance context
  • Budget-conscious deployments - Lower computational cost
  • English-language markets - Keyword detection optimized for English
Example scenarios:
  • Sales outreach to business phone numbers
  • Appointment reminders to office lines
  • B2B lead qualification campaigns
May struggle with:
  • Personal, creative voicemail greetings (“Hey, it’s Mike, you know what to do”)
  • Very short greetings
  • Voicemail language doesn’t match transcriber language
  • Greetings that sound conversational (“Hello? Hello? Just kidding, leave a message”)
  • Background music or noise in greeting
False positives: Human who starts with “You’ve reached…” might be misclassifiedFalse negatives: Voicemail without keywords might be classified as human

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ML-Based AMD (Optional Addon)

Role: Optional fast detection layer you can enable for speed. Works in parallel with text-based AMD. How it works: 
1. Call connects
2. Deep Neural Network (DNN) analyzes audio in real-time:
   - Speech patterns and cadence
   - Voicemail audio patterns
   - Acoustic characteristics
   - Timing and rhythm
   - Natural vs. recorded speech indicators
3. Model trained on tens of thousands of audio recordings
4. Classification: HUMAN or MACHINE
5. Language-independent detection
Characteristics:
Fast: ~1.5 secondsIdentifies live human responses within 1.5 secondsMuch faster than text-based AMD which must wait for complete utterance
Very high accuracy in real-world conditionsWhy enable ML-Based AMD:
  • Language-independent: Works across all languages (text-based only works if transcriber language matches)
  • Detects beep-only voicemail: Catches voicemail systems with no greeting (text-based cannot)
  • Handles creative greetings: Detects personal/non-standard greetings without keywords
  • Pattern-based detection: Doesn’t rely on specific voicemail keywords
  • Fast detection: ~1.5 seconds vs 5-15+ seconds for text-only
  • Better for multi-language campaigns: No language configuration needed
Limitations:
  • Extremely short connections (< 0.5 seconds of audio)
  • Highly degraded audio quality
Ideal for:
  • Consumer campaigns - Personal voicemails with creative greetings
  • Multi-language campaigns - Not dependent on English keywords
  • Quality-focused campaigns - When accuracy is more important than speed
  • Complex markets - Mixed business/personal numbers
Example scenarios:
  • Consumer sales calls
  • Political campaigns
  • Non-profit fundraising
  • Healthcare outreach
  • Multi-language support campaigns
Handles well:
  • Creative personal greetings
  • Short greetings
  • Non-English voicemails
  • Greetings without standard keywords
  • Background music or sound effects
  • Natural conversational-sounding greetings
Robust across:
  • Different languages
  • Regional accents
  • Various voicemail systems
  • Custom greetings

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How AMD Works

Text-Based AMD (Base Layer):
  • Default campaign and phone-test mode
  • Analyzes transcription for voicemail keywords
  • Waits for complete utterance (5-15+ seconds)
  • More conservative - rarely hangs up on live people
ML-Based AMD (Optional Addon):
  • You can optionally enable this for faster detection
  • Analyzes audio patterns in ~1.5 seconds
  • Works in parallel with text-based AMD
  • Faster but may occasionally hang up on live people
Configuration Options: Text-Based Only (Conservative):
  • Only text-based detection active
  • Slower detection (5-15+ seconds)
  • Rarely hangs up on live people
  • Trade-off: Might miss some voicemails and talk to them
  • Best for: When you want to avoid hanging up on live people at all cost
Text-Based + ML-Based (Fast & Recommended):
  • ML detects in ~1.5 seconds
  • Text-based validates in parallel
  • Very high accuracy
  • Trade-off: Occasionally might hang up on a live person
  • Best for: Campaigns where talking to voicemail incurs additional cost
Which should you choose?Text-Based (recommended for most use cases): Sufficient for the majority of campaigns. Rarely hangs up on live people, and handles standard voicemail greetings well.Text-Based + ML-Based: If you need faster detection (~1.5s vs 5-15s) and can tolerate occasionally hanging up on a live person — for example, high-volume campaigns where talking to voicemail incurs meaningful cost.

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Configuring AMD

AMD can be configured in two places:

Test Phone Calls

Enable AMD when testing your agent with phone calls

Campaign Settings

Configure AMD for outbound campaigns

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Test Phone Calls

Configure AMD when testing your agent via phone:
1

Open Agent

Go to your Agent pageClick Test Agent
2

Select phone call

Choose Phone call as the test type
3

Configure AMD

Find the Answering Machine Detection (AMD) settingChoose between:
  • Text-based (default) - Avoids hanging up on live people at all cost
  • ML-based - Fast detection (~1.5s) but may occasionally hang up on live people
4

Make test call

Select your From NumberEnter To Number (your phone number for testing)Click Start Phone CallIf the agent detects voicemail, it hangs up

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Campaign Creation And Settings

Campaign creation uses the default text-based AMD mode. To change AMD for a campaign, open the campaign after creation and use the campaign Settings tab.
1

Campaign Creation

Go to Campaigns sectionClick Create Campaign and fill in the required campaign name, agent, phone number, and schedule fields.
2

Open campaign settings

Open the campaign and select the Settings tab.
3

Select AMD strategy

Switch to Expert mode if needed, then find Answering Machine Detection.Choose voicemail detection strategy for this campaignSelect one:
  • Text-based - Avoids hanging up on live people at all cost (slower, 5-15s)
  • ML-based - Fast detection (~1.5s) but may occasionally hang up on live people
4

Save

The campaign settings page auto-saves the selected strategy.
Changing AMD settings for existing campaigns:
  1. Navigate to campaign Settings
  2. Switch to Expert mode if the AMD field is hidden
  3. Locate Answering Machine Detection (AMD) dropdown
  4. Select different strategy (Text-based or ML-based)
  5. Save changes
Changing AMD settings mid-campaign may affect analytics consistency. Consider creating a new campaign if you need to A/B test AMD configurations.

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AMD Behavior

When AMD detects voicemail, the agent automatically hangs up and logs the outcome. The platform marks the call as MACHINE in campaign analytics, allowing you to schedule retries at different times to increase chances of reaching a live person.

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Testing AMD Configuration

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AMD Test Plan

1

Test ML-Based AMD

Setup:
  1. Configure agent with ML-Based AMD enabled
  2. Prepare test phone number with voicemail
Test:
  1. Start test call to voicemail number
  2. Let call go to voicemail
  3. Monitor agent behavior
Validation:
  • Agent hangs up within ~1.5 seconds
  • Call marked as MACHINE in logs
  • No conversation attempt with voicemail greeting
2

Test Text-Based AMD

Setup:
  1. Configure agent with Text-Based AMD only
  2. Use same voicemail test number
Test:
  1. Start test call
  2. Let call go to voicemail with standard greeting
Validation:
  • Agent waits for complete greeting (5-15+ seconds)
  • Agent hangs up after detecting keywords
  • Call marked as MACHINE
3

Test Live Person Detection

Setup:
  1. Test with both AMD methods
  2. Answer call personally
Test:
  1. Start test call
  2. Answer and say “Hello?”
  3. Verify agent continues conversation normally
Validation:
  • Agent does NOT hang up
  • Normal conversation proceeds
  • Call NOT marked as MACHINE
4

Test Edge Cases

Scenarios to test:Silent answer:
  • Answer but don’t speak
  • Verify AMD doesn’t misclassify
Quick greeting:
  • Answer with very brief “Hi”
  • Verify conversation continues
Voicemail without keywords:
  • Test with non-standard greeting
  • Monitor ML vs text-based performance
Beep-only voicemail:
  • Voicemail system with no greeting
  • Verify ML-based detects, text-based may miss

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Troubleshooting

Symptoms: Performance not matching expectationsCheck:
  • Review campaign AMD setting
  • Compare expected vs actual detection speed
  • Check false positive/negative rates in logs
Solution:
  • Switch between Text-based and ML-based
  • Test both methods with your call patterns
  • Choose based on your priority (speed vs conservative)
Symptoms: Hanging up on live people frequentlyAnalysis:
  • Review call recordings of false positives
  • Check if ML-based AMD is being too aggressive
  • Identify common patterns (background noise, specific greetings)
Solution:
  • Switch to Text-based AMD (more conservative)
  • Improve call quality/reduce background noise
  • Test from different phone numbers
  • Contact support if persistent
Symptoms: Agent frequently talks to voicemailAnalysis:
  • Check if voicemails have non-standard greetings
  • Review if beep-only voicemail systems
  • Verify transcriber language matches voicemail language
Solution:
  • Switch to ML-based AMD (better for non-standard greetings)
  • ML-based detects beep-only systems
  • Ensure agent speaks same language as target audience

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Next Steps

Campaign Management

Create and manage outbound calling campaigns

Campaign Management

Track AMD performance and optimize campaigns

Prompt

Write effective prompts for call handling

Schedules

Configure optimal calling times based on AMD data