{"id":2859,"date":"2026-09-05T05:07:59","date_gmt":"2026-09-05T05:07:59","guid":{"rendered":"https:\/\/www.plura.ai\/articles\/how-vapi-handles-voicemail-detection"},"modified":"2026-09-05T05:07:59","modified_gmt":"2026-09-05T05:07:59","slug":"how-vapi-handles-voicemail-detection","status":"publish","type":"post","link":"https:\/\/www.plura.ai\/articles\/how-vapi-handles-voicemail-detection","title":{"rendered":"How Vapi Handles Voicemail Detection: A Configuration Guide"},"content":{"rendered":"<p><em>Written by: Matt Beucler, CEO, Plura AI<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>Vapi supports two voicemail detection methods: built-in AI analysis with carrier signals, and a configurable Voicemail Tool that uses an LLM for context-aware decisions.<sup data-disclaimer-id=\"25\" data-disclaimer-index=\"4\">4<\/sup><\/li>\n<li>Voicemail detection accuracy directly affects outbound performance because misclassified calls lower conversion rates and affect TCPA compliance posture.<sup data-disclaimer-id=\"22\" data-disclaimer-index=\"1\">1<\/sup><\/li>\n<li>Built-in detection suits fast, basic human-versus-voicemail decisions, while the Voicemail Tool supports complex flows that include IVRs and call screeners.<\/li>\n<li>Key configuration parameters include <code>startAtSeconds<\/code>, <code>frequencySeconds<\/code>, <code>maxRetries<\/code>, and <code>beepMaxAwaitSeconds<\/code>, which you tune to your campaign and target voicemail systems.<\/li>\n<\/ul>\n<h2>Why Voicemail Detection Accuracy Drives Outbound Results<\/h2>\n<p>False positives classify a live human as voicemail, which causes agents or AI to hang up or play a voicemail script to someone who answered. That behavior lowers conversion and damages customer experience. False negatives miss a voicemail greeting, so the AI starts speaking before the beep and the recording cuts off key details like the callback number.<\/p>\n<p>Accurate detection improves contact rates, agent utilization, and TCPA (Telephone Consumer Protection Act) compliance posture by helping teams leave messages only on actual answering machines.<sup data-disclaimer-id=\"22\" data-disclaimer-index=\"1\">1<\/sup> Industry data indicates that roughly 70-80% of outbound sales calls go unanswered or reach voicemail, with RingDNA (2025) reporting 72% of outbound sales calls going to voicemail, though other studies cite figures ranging from 41% to 87% depending on methodology and context, so detection accuracy becomes a primary lever on campaign economics.<sup data-disclaimer-id=\"24\" data-disclaimer-index=\"3\">3<\/sup><\/p>\n<p>This guide consolidates Vapi\u2019s documented capabilities into an operational decision framework that supports configuration choices for high-volume teams.<\/p>\n<h2>How Vapi\u2019s Built-In Voicemail Detection Works<\/h2>\n<p>Vapi\u2019s built-in detection pairs a Gemini-based audio classifier with optional beep detection and polls on a backoff plan with a 2.5-second floor. The system also uses carrier-level answering machine detection (AMD) signals. When you use Twilio as the telephony provider, Vapi delegates to Twilio\u2019s AMD, which returns outcomes such as <code>human<\/code>, <code>machine_start<\/code>, <code>machine_end_beep<\/code>, <code>machine_end_silence<\/code>, and <code>unknown<\/code>.<\/p>\n<p>Detection runs on a polling schedule with a configurable start delay and frequency. It monitors the opening seconds of the call and classifies based on speech duration, silence patterns, and greeting phrasing. A short \u201cHello?\u201d with rising intonation reads as human. A greeting like \u201cYou\u2019ve reached the voicemail of\u2026\u201d triggers machine classification.<\/p>\n<p>Built-in detection has documented limitations. <a href=\"https:\/\/callsphere.ai\/blog\/voicemail-detection-accuracy-callsphere-vs-vapi\" target=\"_blank\" rel=\"noindex nofollow\">Vapi\u2019s built-in voicemail detection has no second-pass LLM verification, no way to inject domain knowledge about specific voicemail systems, and limited debugging visibility into why a false positive occurred.<\/a><\/p>\n<h2>The Voicemail Tool for Context-Aware Detection<\/h2>\n<p>Vapi\u2019s Voicemail Tool uses an LLM to analyze the conversation transcript and decide whether the call reached voicemail. The tool runs as a callable function that the assistant LLM can invoke during a call, with explicit instructions for how to react when it detects an auto-attendant or voicemail system.<\/p>\n<p>Vapi\u2019s documentation recommends LLM-based detection via function calling (the Vapi Voicemail Tool, beta) as the most customizable and cost-efficient option, while marking Twilio AMD as legacy and recommending it only in special cases; however, Vapi\u2019s automatic detection provider (Vapi, Recommended) remains the default and is \u201cstrongly recommended\u201d for general use.<\/p>\n<p>Key differences from built-in detection include the following points.<\/p>\n<ul>\n<li>The LLM provides flexible, context-aware classification and can distinguish voicemail greetings, IVR (interactive voice response) menus, hold music, and carrier screeners.<\/li>\n<li>Detection may run slower because the system must transcribe and analyze speech before returning a decision.<\/li>\n<li>The Voicemail Tool requires more configuration, including tool definition, prompts, and actions to take when it detects voicemail or an auto-attendant.<\/li>\n<\/ul>\n<p>Vapi\u2019s documentation recommends one detection method per assistant. Built-in detection fits straightforward voicemail-or-human decisions and prioritizes speed. The Voicemail Tool fits complex scenarios that involve voicemail, IVR systems, and call screeners, with additional setup and latency as the trade-off.<\/p>\n<p>Once you select a detection method, the next step is tuning its configuration parameters so they match your campaign\u2019s call patterns and the voicemail systems you encounter most often.<\/p>\n<h2>Configuration Parameters for Reliable Detection<\/h2>\n<p>Vapi exposes voicemail detection configuration through a <code>voicemailDetection<\/code> object in the assistant settings. The parameters work together to control how long the system listens, how often it checks for voicemail, and how it reacts to silence and beeps.<\/p>\n<h3>Built-In Detection Parameters with Twilio<\/h3>\n<pre><code>{ \"voicemailDetection\": { \"enabled\": true, \"provider\": \"twilio\", \"machineDetectionTimeout\": 30, \"machineDetectionSpeechThreshold\": 2400, \"machineDetectionSpeechEndThreshold\": 1200, \"machineDetectionSilenceTimeout\": 5000 } }<\/code><\/pre>\n<p>The first group of parameters controls Twilio AMD behavior and the overall detection window.<\/p>\n<ul>\n<li><strong><code>machineDetectionTimeout<\/code><\/strong> (default 30 seconds) sets how long Vapi and Twilio wait for AMD to classify the call before stopping. <a href=\"https:\/\/callsphere.ai\/blog\/voicemail-detection-accuracy-callsphere-vs-vapi\" target=\"_blank\" rel=\"noindex nofollow\">Longer values increase the chance of a result but delay call progress.<\/a><\/li>\n<li><strong><code>machineDetectionSpeechThreshold<\/code><\/strong> (default 2400 ms) defines the duration of continuous speech required before the system classifies the callee as a machine. <a href=\"https:\/\/callsphere.ai\/blog\/voicemail-detection-accuracy-callsphere-vs-vapi\" target=\"_blank\" rel=\"noindex nofollow\">Higher values reduce false machine detections but can miss short voicemail greetings.<\/a><\/li>\n<li><strong><code>machineDetectionSpeechEndThreshold<\/code><\/strong> (default 1200 ms) controls how much silence must follow speech before the machine classification can finalize. Lower values risk misclassifying voicemail greetings with pauses as human responses.<\/li>\n<li><strong><code>machineDetectionSilenceTimeout<\/code><\/strong> (default 5000 ms) sets how long silence can persist before AMD stops waiting. <a href=\"https:\/\/callsphere.ai\/blog\/voicemail-detection-accuracy-callsphere-vs-vapi\" target=\"_blank\" rel=\"noindex nofollow\">Shorter values make decisions faster but can mis-handle quiet or delayed pickups.<\/a><\/li>\n<\/ul>\n<p>These parameters interact in practice. The timeout caps the total listening window, while the speech and silence thresholds shape how aggressively the system labels long greetings as machines.<\/p>\n<h3>Voicemail Tool Timing Parameters<\/h3>\n<pre><code>{ \"type\": \"voicemail\", \"startAtSeconds\": 2, \"frequencySeconds\": 2.5, \"maxRetries\": 5, \"beepMaxAwaitSeconds\": 30 }<\/code><\/pre>\n<p>The Voicemail Tool timing parameters control when detection starts, how often it runs, and how long it waits for the beep.<\/p>\n<ul>\n<li><strong><code>startAtSeconds<\/code><\/strong> sets the delay before detection begins after call connection. Vapi\u2019s voicemail detection documentation specifies a startAtSeconds of 1.5 for the Lead Qualification use case, and recommends increasing startAtSeconds to 3-4 seconds to reduce false positives (false-machine classifications).<\/li>\n<li><strong><code>frequencySeconds<\/code><\/strong> defines how often the system rechecks for voicemail. Vapi enforces a practical minimum of 2.5 seconds. Shorter intervals catch voicemail cues faster but increase false positives.<\/li>\n<li><strong><code>maxRetries<\/code><\/strong> sets the maximum number of detection attempts before stopping. In Vapi\u2019s voicemail detection, lower maxRetries values such as 4-5 suit faster detection in high-volume campaigns, while higher values such as 8-10 support improved accuracy by observing more of the greeting, though the documentation does not prescribe exact ranges.<\/li>\n<li><strong><code>beepMaxAwaitSeconds<\/code><\/strong> controls how long the system waits for a voicemail beep before speaking a message. Values range from 0-60 seconds; 25-30 seconds is typical because greetings commonly run 10-20 seconds before the tone. Setting it under 15 seconds risks the voicemail system cutting off the beginning of your message.<\/li>\n<\/ul>\n<p>In combination, these settings balance speed against accuracy. Earlier start times and more retries capture more greetings, while longer beep waits protect message delivery at the cost of slightly longer calls.<\/p>\n<h2>Provider Options and Selection Guidance<\/h2>\n<p>Vapi supports several voicemail detection providers, each with distinct trade-offs. <a href=\"https:\/\/vc.ru\/dev\/2920689-voicemail-detektirovanie-v-ai-zvonkakh\" target=\"_blank\" rel=\"noindex nofollow\">Vapi\u2019s documented options include Twilio AMD (marked as legacy), Google (based on Google Speech), OpenAI (used in newer configurations), and Vapi\u2019s built-in detection that uses Gemini-based classification.<\/a><\/p>\n<p>Vapi\u2019s own provider combines Gemini audio classification with optional beep detection and a polling schedule that respects a 2.5-second floor. Twilio AMD uses carrier-level detection and, according to Twilio\u2019s documentation, MachineDetection=DetectMessageEnd can reach high accuracy in US destinations with typical results arriving within about four seconds after answer. Google\u2019s option uses Google Speech recognition in a newer configuration path. The OpenAI provider uses LLM-based detection that supports flexible phrasing and transcript-based decisions at a higher cost profile.<\/p>\n<p>Selection typically depends on geography, latency tolerance, and engineering capacity. Twilio AMD suits straightforward US and Canada campaigns but appears as a legacy option in Vapi\u2019s docs. Google and OpenAI providers support more sophisticated analysis with additional latency. Vapi\u2019s built-in provider offers a hybrid approach that pairs Gemini classification with beep detection. Teams should test providers against their own call lists because vendor defaults reflect different caller mixes.<\/p>\n<h2>Common Failure Modes and Troubleshooting<\/h2>\n<h3>False Positives: Humans Classified as Voicemail<\/h3>\n<p><strong>Symptoms:<\/strong> The AI hangs up on live prospects or delivers a voicemail script to a person who answered.<\/p>\n<p><strong>Causes:<\/strong> Long business greetings or slow and non-native speakers can cross the speech threshold and read as machines. Detection that starts too early increases this risk.<\/p>\n<p><strong>Fixes:<\/strong> To reduce false positives in Vapi voicemail detection, increase startAtSeconds to 3-4 seconds, increase frequencySeconds to 3-4 seconds, or switch to Google or OpenAI provider.<\/p>\n<h3>Cut-Off Messages: AI Speaks Before the Beep<\/h3>\n<p><strong>Symptoms:<\/strong> The voicemail recording captures only part of the message and often misses the callback number.<\/p>\n<p><strong>Causes:<\/strong> The <code>beepMaxAwaitSeconds<\/code> value sits below the greeting length for the target voicemail system, so the AI starts talking too early.<\/p>\n<p><strong>Fixes:<\/strong> Increase <code>beepMaxAwaitSeconds<\/code> to 25-30 seconds. Test against your actual list because greeting length varies by carrier and demographic.<\/p>\n<h3>Missed Voicemails: Detection Never Triggers<\/h3>\n<p><strong>Symptoms:<\/strong> The AI talks to a recording as if it were a human, or the call disconnects without leaving a message.<\/p>\n<p><strong>Causes:<\/strong> Missed voicemails in Vapi can result from a startAtSeconds value that is too high and a maxRetries value that is too low; the docs recommend decreasing startAtSeconds to 1-2 seconds and increasing maxRetries to 8-10.<\/p>\n<p><strong>Fixes:<\/strong> Decrease <code>startAtSeconds<\/code> to 1-1.5 seconds, increase <code>maxRetries<\/code> to 8-10, keep <code>frequencySeconds<\/code> at the 2.5-second minimum, and enable beep detection when available.<\/p>\n<h3>IVR and Call Screeners Misclassified<\/h3>\n<p><strong>Symptoms:<\/strong> Auto-attendant menus trigger voicemail messages that nobody hears, and iOS or Pixel call screening prompts are treated as human conversations.<\/p>\n<p><strong>Causes:<\/strong> <a href=\"https:\/\/rockydialer.com\/blog\/answering-machine-detection\/\" target=\"_blank\" rel=\"noindex nofollow\">Rule-based AMD can distinguish an IVR menu from a human receptionist in many cases, though it may misclassify some scripted human greetings as machines; modern AMD systems classify IVR menus as a distinct machine category rather than as human.<\/a><\/p>\n<p><strong>Fixes:<\/strong> Use the Voicemail Tool with LLM-based detection for these scenarios, or log screener pickups as their own outcome bucket so they do not distort reachability reporting.<\/p>\n<h2>Best Practices for Outbound Campaigns<\/h2>\n<ul>\n<li><strong>Use a single detection owner per assistant.<\/strong> Running carrier-level AMD and the Voicemail Tool together produces conflicting verdicts. One system should own the decision.<\/li>\n<li><strong>Treat uncertain classifications as human.<\/strong> Disconnecting on a customer costs more than a wasted minute. When detection returns <code>unknown<\/code>, treat the call as a live human.<\/li>\n<li><strong>Test with real calls from your list.<\/strong> Voicemail detection is an audio problem. Lab recordings from a laptop microphone do not predict cellular call behavior. Sample 50 recordings from your actual list and measure where detection lands.<\/li>\n<li><strong>Monitor detection accuracy over time.<\/strong> Voicemail patterns drift as carriers update prompts and greeting styles change. Re-run your test suite against a fresh sample monthly and re-baseline after any telephony migration.<\/li>\n<li><strong>Align detection with your compliance framework.<\/strong> The FCC\u2019s February 2024 Declaratory Ruling describes how AI-generated voices relate to TCPA\u2019s \u201cartificial or prerecorded voice\u201d provisions.<sup data-disclaimer-id=\"23\" data-disclaimer-index=\"2\">2<\/sup> <a href=\"https:\/\/www.dwt.com\/-\/media\/files\/2024\/02\/fcc2417a1.pdf?hash=68F82B792F83E64AD70181CCB96CC4FE&amp;rev=574f829d9ec8495e8d8e8431c38b3827\" target=\"_blank\" rel=\"noindex nofollow\">Under the FCC\u2019s February 2024 Declaratory Ruling, AI-generated voice calls fall within that category and the ruling describes consent, identification, disclosure, and opt-out expectations when the call includes an advertisement or constitutes telemarketing.<\/a> This description summarizes the framework and does not provide legal advice; consult qualified counsel for your specific campaigns.<\/li>\n<\/ul>\n<h2>How Plura AI Supports Managed Voicemail Detection<\/h2>\n<p>Plura AI serves teams that prefer a managed approach instead of owning every integration detail themselves. Vapi provides powerful primitives and documentation, and your team assembles conversation design, telephony configuration, voicemail detection tuning, and compliance workflows.<\/p>\n<p>Plura provides a managed, carrier-grade alternative. The platform runs on <a href=\"https:\/\/plura.ai\" target=\"_blank\" rel=\"noindex nofollow\">Plura\u2019s own FCC-licensed audio bridging carrier<\/a>. Voicemail detection, branded caller ID, and compliance support sit inside the platform rather than as separate components. <a href=\"https:\/\/plura.ai\" target=\"_blank\" rel=\"noindex nofollow\">STIR\/SHAKEN caller ID verification runs on outbound calls at the carrier level<\/a>.<sup data-disclaimer-id=\"22\" data-disclaimer-index=\"1\">1<\/sup><\/p>\n<p>For teams that want managed voicemail detection as part of a broader outbound stack, Plura\u2019s <a href=\"https:\/\/plura.ai\/ai-predictive-dialer\" target=\"_blank\" rel=\"noindex nofollow\">AI Predictive Dialer<\/a> includes detection tuning handled by Plura\u2019s engineering team. Plura supports compliance through features such as <a href=\"https:\/\/plura.ai\/products\/compliance\" target=\"_blank\" rel=\"noindex nofollow\">real-time DNC scrubbing, TCPA consent logging, and quiet-hours enforcement<\/a>, while customers remain responsible for their own regulatory obligations and legal positions.<sup data-disclaimer-id=\"22\" data-disclaimer-index=\"1\">1<\/sup><\/p>\n<p>Compare <a href=\"https:\/\/plura.ai\/pricing\" target=\"_blank\">plans and rates<\/a> or run your numbers through Plura\u2019s <a href=\"https:\/\/plura.ai\/calculator\" target=\"_blank\">ROI calculator<\/a> to estimate the impact of managed voicemail detection at your call volume.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What does \u201cvoicemail detected\u201d mean in Vapi?<\/h3>\n<p>When Vapi\u2019s detection system classifies a call as voicemail, the AI model or carrier-level AMD has determined that an automated system answered instead of a live human. Vapi then executes the configured action, such as leaving a pre-recorded or dynamically generated message or hanging up, based on your assistant settings. The specific action depends on how you configured the <code>voicemailDetection<\/code> object or the Voicemail Tool in your assistant definition.<\/p>\n<h3>Can Vapi detect voicemail without the Voicemail Tool?<\/h3>\n<p>Yes. Vapi\u2019s built-in voicemail detection uses AI model analysis and carrier-level signals such as Twilio\u2019s AMD to identify voicemail greetings without the Voicemail Tool. The built-in path combines Gemini-based audio classification with optional beep detection and polls on a configurable schedule. The Voicemail Tool adds an LLM-based detection path that supports more context-aware classification for complex scenarios such as IVR menus and call screeners. Vapi\u2019s documentation recommends choosing one path per assistant rather than running both at the same time.<\/p>\n<h3>How do I troubleshoot Vapi voicemail detection not working?<\/h3>\n<p>Start with your configuration parameters. Confirm that <code>startAtSeconds<\/code> is not set so late that it misses the greeting, <code>frequencySeconds<\/code> sits at the 2.5-second minimum, <code>maxRetries<\/code> is high enough for your accuracy target, and <code>beepMaxAwaitSeconds<\/code> is at least 25-30 seconds. Test with real calls from your actual list because voicemail detection depends on live audio conditions. If false positives persist, increase <code>machineDetectionSpeechThreshold<\/code> or evaluate a different provider. If voicemails are being missed, decrease <code>startAtSeconds<\/code> to 1-1.5 seconds and increase <code>maxRetries<\/code>. Log every misclassified call as a regression fixture so you can validate future configuration changes against known edge cases.<\/p>\n<h3>Should I enable both built-in detection and the Voicemail Tool?<\/h3>\n<p>Vapi\u2019s documentation and third-party guidance recommend a single active voicemail detection system per assistant. Running carrier-level AMD and the Voicemail Tool together can produce conflicting verdicts, and Vapi does not document tie-breaking logic. Choose one detection owner based on your use case: built-in detection for speed and simplicity on straightforward voicemail-or-human decisions, or the Voicemail Tool for context-aware classification of complex scenarios that include IVR systems and call screeners.<\/p>\n<h3>What is the difference between <code>machineDetectionTimeout<\/code> and <code>beepMaxAwaitSeconds<\/code>?<\/h3>\n<p><code>machineDetectionTimeout<\/code> controls how long the system waits for AMD to classify the call before it stops listening. It caps the total detection window and determines whether you receive any verdict. <code>beepMaxAwaitSeconds<\/code> controls how long the system waits for the voicemail beep before speaking a message, and it applies after the call is already classified as voicemail. The first parameter governs whether you get a classification, and the second governs whether your message starts cleanly after the beep instead of being cut off.<\/p>\n<h2>Conclusion: Match Detection Architecture to Your Campaign<\/h2>\n<p>Vapi gives teams strong voicemail detection building blocks, including built-in AI analysis, carrier-level AMD, and an LLM-based Voicemail Tool. Your team assembles these components into a reliable system that fits your outbound strategy.<\/p>\n<p>Teams that prefer a managed solution can use Plura as a carrier-grade alternative with <a href=\"https:\/\/plura.ai\/ai-predictive-dialer\" target=\"_blank\" rel=\"noindex nofollow\">voicemail detection<\/a> built into the platform. You can compare <a href=\"https:\/\/plura.ai\/pricing\" target=\"_blank\">plans and rates<\/a>, run projections with the <a href=\"https:\/\/plura.ai\/calculator\" target=\"_blank\">ROI calculator<\/a>, or <a href=\"https:\/\/www.plura.ai\/plura-webchat\" target=\"_blank\">book a live demo with Plura<\/a> to see the platform in action.<\/p>\n<hr data-disclaimer-divider=\"true\">\n<div data-disclaimer-footer=\"true\">\n<p data-disclaimer-id=\"22\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"1\">1<\/sup> Plura AI maintains SOC 2, HIPAA, ISO, and GDPR posture as part of its platform infrastructure. References to compliance frameworks in this article describe Plura\u2019s platform capabilities and do not constitute a guarantee that any customer using Plura will themselves be compliant with applicable laws or standards. Customers remain solely responsible for their own regulatory obligations, certifications, consent management, recordkeeping, and the claims they make to their own end users. Consult qualified legal counsel for guidance specific to your use case.<\/p>\n<p data-disclaimer-id=\"23\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"2\">2<\/sup> This article describes regulatory frameworks at a general level and does not constitute legal advice. Laws and regulations vary by jurisdiction, change over time, and apply differently depending on facts and circumstances. Readers should consult qualified legal counsel before making compliance decisions.<\/p>\n<p data-disclaimer-id=\"24\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"3\">3<\/sup> Performance figures, customer outcomes, and industry statistics referenced in this article are drawn from cited third-party sources or Plura customer case studies. Individual results vary based on implementation, use case, industry, audience, and execution. Past or aggregate performance is not a guarantee of future results.<\/p>\n<p data-disclaimer-id=\"25\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"4\">4<\/sup> References to third-party products, services, companies, or research are made for informational and comparative purposes only. Plura AI is not affiliated with, endorsed by, or sponsored by any third party named in this article unless explicitly stated. Trademarks and product names referenced remain the property of their respective owners.<\/p>\n<p data-disclaimer-id=\"21\" data-disclaimer-type=\"fixed\">This article is provided for informational purposes only and reflects Plura AI\u2019s understanding at the time of publication. Product capabilities, integrations, and specifications are subject to change. For the most current information, visit plura.ai.<\/p>\n<p data-disclaimer-id=\"27\" data-disclaimer-type=\"fixed\">This article was produced with the assistance of AI tools and reviewed by Plura AI prior to publication.<\/p>\n<\/div>\n<section data-read-next=\"true\">\n<h2>Read Next<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/voicemail-beep-detection\" target=\"_blank\">Voicemail Beep Detection: How It Works and Why It Fails<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/voicemail-detection-voice-ai\" target=\"_blank\">What Is Voicemail Detection in Voice AI?<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/voicemail-detection-integration\" target=\"_blank\">Voicemail Detection Integration for Contact Centers<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/improve-voicemail-detection-accuracy\" target=\"_blank\">How to Improve Voicemail Detection Accuracy: 2026 Guide<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/voicemail-detection-alternative\" target=\"_blank\">Voicemail Detection Alternatives for Outbound Leaders<\/a><\/li>\n<\/ul>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Configure Vapi voicemail detection to improve outbound accuracy. Plura AI manages detection settings so your campaigns connect with more live answers.<\/p>\n","protected":false},"author":106,"featured_media":2858,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[8],"tags":[],"class_list":["post-2859","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-voice-agents"],"_links":{"self":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts\/2859","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/comments?post=2859"}],"version-history":[{"count":0,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts\/2859\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/media\/2858"}],"wp:attachment":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/media?parent=2859"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/categories?post=2859"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/tags?post=2859"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}