Voice Agents That Don’t Break at Enterprise Scale \u2014 SimplAI<\/title><\/p>\n<style> @import url('https:\/\/fonts.googleapis.com\/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap'); *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; } :root { --purple: #7B5EA7; --purple-light: #9B7FBF; --purple-dark: #5a3f88; --purple-accent: #8B6BB1; --dark-bg: #1a1a2e; --dark-card: #16213e; --text-dark: #1a1a1a; --text-mid: #333; --text-light: #555; --text-muted: #777; --border: #e0e0e0; --bg-light: #f8f8f8; --bg-purple-light: #f0ebf8; --white: #ffffff; --stat-bg-1: #2d2d4e; --stat-bg-2: #6b4fa0; --stat-bg-3: #3a3a5c; --stat-bg-4: #4a3870; } body { font-family: 'Inter', sans-serif; background: var(--white); color: var(--text-dark); line-height: 1.6; } \/* PAGE WRAPPER *\/ .page { width: 794px; background: var(--white); margin: 0 auto; position: relative; overflow: hidden; box-shadow: none; border: none; padding: 0; } \/* \u2500\u2500 HEADER (shared across inner pages) \u2500\u2500 *\/ .page-header { display: flex; 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padding-top: 20px; border-top: 1px solid var(--border); } .meta-col .meta-label { font-size: 8px; font-weight: 700; color: var(--purple); letter-spacing: 0.1em; text-transform: uppercase; margin-bottom: 6px; } .meta-col p { font-size: 10px; color: var(--text-mid); line-height: 1.7; } \/* \u2500\u2500 COVER PAGE \u2500\u2500 *\/ .cover-top { background: var(--dark-bg); padding: 20px 28px 16px; display: flex; justify-content: space-between; align-items: flex-start; } .cover-logo { display: flex; align-items: center; gap: 10px; } .cover-logo-icon { width: 36px; height: 36px; background: var(--purple); border-radius: 10px; display: flex; align-items: center; justify-content: center; } .cover-logo-icon svg { width: 22px; height: 22px; fill: white; } .cover-brand-name { font-size: 16px; font-weight: 800; color: white; } .cover-brand-sub { font-size: 10px; color: rgba(255,255,255,0.6); } .cover-badge { text-align: right; } .cover-badge .badge-line1 { font-size: 9px; font-weight: 700; color: var(--purple-light); 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} .cover-desc { font-size: 12px; color: rgba(255,255,255,0.75); max-width: 460px; line-height: 1.7; margin: 20px 0 0; } .cover-number-big { position: absolute; top: -10px; right: -20px; font-size: 280px; font-weight: 900; color: rgba(255,255,255,0.04); line-height: 1; z-index: 0; } .cover-meta-bar { background: white; padding: 18px 36px; display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 20px; border-bottom: 1px solid var(--border); } .cover-meta-col .cm-label { font-size: 8px; font-weight: 700; color: var(--purple); letter-spacing: 0.1em; text-transform: uppercase; margin-bottom: 4px; } .cover-meta-col .cm-val { font-size: 11px; font-weight: 600; color: var(--text-dark); } .cover-meta-col .cm-sub { font-size: 10px; color: var(--text-muted); } .cover-stats { padding: 22px 36px 28px; background: white; } .cover-stats-label { font-size: 8px; font-weight: 700; color: var(--purple); letter-spacing: 0.12em; text-transform: uppercase; margin-bottom: 14px; } .cover-stats-row { display: grid; grid-template-columns: 1fr 1fr 1fr 1fr; gap: 10px; } .cover-stat-box { padding: 14px; border-radius: 5px; color: white; } .cover-stat-box.c1 { background: var(--stat-bg-1); } .cover-stat-box.c2 { background: var(--stat-bg-2); } .cover-stat-box.c3 { background: var(--stat-bg-3); } .cover-stat-box.c4 { background: var(--stat-bg-4); } .csb-label { font-size: 7px; font-weight: 700; letter-spacing: 0.1em; text-transform: uppercase; color: rgba(255,255,255,0.7); margin-bottom: 4px; } .csb-value { font-size: 24px; font-weight: 900; line-height: 1.1; margin-bottom: 4px; } .csb-sub { font-size: 8px; color: rgba(255,255,255,0.6); line-height: 1.4; } \/* page 2 - TOC + Intro *\/ .toc-intro { display: grid; grid-template-columns: 200px 1fr; gap: 36px; padding: 28px 36px 0px; } .toc-box { } .toc-box h3 { font-size: 9px; font-weight: 700; color: var(--purple); letter-spacing: 0.1em; text-transform: uppercase; margin-bottom: 14px; border-bottom: 2px solid var(--purple); padding-bottom: 6px; display: inline-block; } .toc-list { list-style: none; } .toc-list li { display: flex; align-items: flex-start; gap: 8px; padding: 4px 0; border-bottom: 1px solid var(--border); font-size: 10px; color: var(--text-mid); } .toc-list li .toc-num { color: var(--purple); font-weight: 700; font-size: 9px; min-width: 16px; } .intro-box { } .intro-box .intro-label { font-size: 8px; font-weight: 700; color: var(--purple); letter-spacing: 0.12em; text-transform: uppercase; margin-bottom: 10px; padding-bottom: 4px; border-bottom: 2px solid var(--purple); display: inline-block; } .intro-box h2 { font-size: 26px; font-weight: 800; margin-bottom: 16px; } \/* Methodology table *\/ .method-table { width: 100%; border-collapse: collapse; margin: 18px 0; } .method-table thead tr { background: transparent; border-bottom: 1px solid var(--border); } .method-table thead th { padding: 6px 10px; text-align: left; font-size: 8.5px; font-weight: 700; color: var(--purple); letter-spacing: 0.1em; text-transform: uppercase; } .method-table tbody tr { border-bottom: 1px solid var(--border); } .method-table tbody td { padding: 10px; font-size: 10.5px; color: var(--text-mid); vertical-align: top; } .method-table tbody td .mt-title { font-weight: 700; color: var(--text-dark); margin-bottom: 2px; } .method-table tbody td .mt-sub { font-size: 9.5px; color: var(--text-muted); } \/* Where latency lives box *\/ .highlight-box { background: var(--bg-purple-light); border-left: 3px solid var(--purple); padding: 14px 16px; margin: 14px 0; } .highlight-box .hl-label { font-size: 8px; font-weight: 700; color: var(--purple); letter-spacing: 0.1em; text-transform: uppercase; margin-bottom: 6px; } .highlight-box p { font-size: 10.5px; color: var(--text-mid); line-height: 1.65; } \/* Commitment page *\/ .toc-list li a { color: var(--text-mid); text-decoration: none; } .toc-list li a:hover { color: var(--purple); text-decoration: underline; }<\/style>\n<p><\/head><body><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"> <svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg> <\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"toc-intro\">\n<div class=\"toc-box\">\n<h3>Contents<\/h3>\n<ul class=\"toc-list\">\n<li><span class=\"toc-num\">01<\/span> <a href=\"\/#s01\">The Voice Agent, End-to-End<\/a><\/li>\n<li><span class=\"toc-num\">02<\/span> <a href=\"\/#s02\">How We Benchmarked<\/a><\/li>\n<li><span class=\"toc-num\">03<\/span> <a href=\"\/#s03\">The Media Plane Holds: SFU at 1,000 Rooms<\/a><\/li>\n<li><span class=\"toc-num\">04<\/span> <a href=\"\/#s04\">SIP Inbound: The Worker Is the Limit<\/a><\/li>\n<li><span class=\"toc-num\">05<\/span> <a href=\"\/#s05\">The Single Biggest Latency Lever<\/a><\/li>\n<li><span class=\"toc-num\">06<\/span> <a href=\"\/#s06\">Picking the Right Speech Vendors<\/a><\/li>\n<li><span class=\"toc-num\">07<\/span> <a href=\"\/#s07\">Inside the Hot Path<\/a><\/li>\n<li><span class=\"toc-num\">08<\/span> <a href=\"\/#s08\">Naturalness: Barge-In and Recovery<\/a><\/li>\n<li><span class=\"toc-num\">09<\/span> <a href=\"\/#s09\">Capacity Plan: 1,000 Concurrent Calls<\/a><\/li>\n<li><span class=\"toc-num\">10<\/span> <a href=\"\/#s10\">What We Ship Next<\/a><\/li>\n<li><span class=\"toc-num\">11<\/span> <a href=\"\/#s11\">The SimplAI Commitment<\/a><\/li>\n<\/ul><\/div>\n<div class=\"intro-box\">\n<div class=\"intro-label\">Introduction<\/div>\n<h2>Why this report exists.<\/h2>\n<p class=\"body-text\"><strong>A voice agent feels effortless when it works.<\/strong> You speak, it listens, it answers \u2014 naturally, without awkward pauses, and without falling over when the next thousand callers join the line. The hard part isn’t the demo. It’s holding that quality on the thousandth concurrent call.<\/p>\n<p class=\"body-text\">At SimplAI, we run voice agents in two flavors that share the same underlying stack: outbound and inbound calling agents that talk to customers over real phone lines, and embedded conversational agents that talk to users in a browser or app over WebRTC. Both demand the same thing \u2014 low latency, human-like turn-taking, and steady behavior under load.<\/p>\n<p class=\"body-text\">Over the past quarter we ran a structured benchmark program against our voice stack: SFU concurrency, SIP inbound capacity, end-to-end Voice Agent Response Time (VART), STT and TTS vendor characterization, agent-pipeline TTFT under load, and barge-in latency. This post is a distilled, no-spin tour of what we measured, where the bottlenecks actually live, and how we’re paving the road from here to reliable 1,000-concurrent enterprise deployments.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>02<\/span> <\/div>\n<\/div>\n<p><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"><svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg><\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/div><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/div><\/div>\n<div class=\"page-body\">\n<div id=\"s01\" class=\"section-label\">Section 01<\/div>\n<h2 class=\"section-title\">The Voice Agent, End-to-End<\/h2>\n<p class=\"body-text\">Whether the caller arrives over a phone line or a browser, the path looks the same once the call is on the wire. Audio enters the LiveKit SFU and a voice-agent worker is dispatched into the room. The worker handles the audio plane (VAD, STT, TTS, turn-taking, barge-in) and forwards each conversational turn to the Agent-Service Orchestration layer, which runs agentic orchestration \u2014 conversation state, tools, function calls \u2014 and makes its LLM calls through the LLM Router. The agent’s reply streams back through the SFU to the caller.<\/p>\n<div class=\"arch-diagram\">\n<div class=\"arch-row\">\n<div class=\"arch-block\">\n<div class=\"ab-title\">Phone \/ SIP<\/div>\n<div class=\"ab-sub\">PSTN inbound & outbound<\/div>\n<\/p><\/div>\n<div class=\"arch-block\">\n<div class=\"ab-title\">Browser \/ App<\/div>\n<div class=\"ab-sub\">WebRTC client<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"arch-row\">\n<div class=\"arch-block purple\">\n<div class=\"ab-title purple-text\">LiveKit SIP<\/div>\n<div class=\"ab-sub\">SIP signaling<\/div>\n<\/p><\/div>\n<div class=\"arch-block purple\">\n<div class=\"ab-title purple-text\">LiveKit SFU<\/div>\n<div class=\"ab-sub\">WebRTC media plane<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"arch-row single\">\n<div class=\"arch-block purple\">\n<div class=\"ab-title purple-text\">Voice-Agent Worker<\/div>\n<div class=\"ab-sub\">VAD \u00b7 turn-taking \u00b7 barge-in<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"arch-row\">\n<div class=\"arch-block\">\n<div class=\"ab-title\">STT<\/div>\n<div class=\"ab-sub\">Deepgram Nova-3 \/ Flux<\/div>\n<\/p><\/div>\n<div class=\"arch-block\">\n<div class=\"ab-title\">TTS<\/div>\n<div class=\"ab-sub\">ElevenLabs Flash v2.5<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"arch-row single\">\n<div class=\"arch-block purple\">\n<div class=\"ab-title purple-text\">Agent-Service \u00b7 Agentic Orchestration<\/div>\n<div class=\"ab-sub\">conversation state \u00b7 tools \u00b7 function calls<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"arch-row\">\n<div class=\"arch-block purple\">\n<div class=\"ab-title purple-text\">LLM Router<\/div>\n<div class=\"ab-sub\">model routing + Model Aggregator<\/div>\n<\/p><\/div>\n<div class=\"arch-block\">\n<div class=\"ab-title\">LLM Provider<\/div>\n<div class=\"ab-sub\">reply audio: TTS \u2192 Worker \u2192 SFU \u2192 Caller<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"arch-caption\">Regions deployed: Central-US \u00b7 Mumbai (APAC). Each block is an independently scalable Kubernetes deployment.<\/div>\n<\/p><\/div>\n<div class=\"two-col\">\n<div class=\"design-card\">\n<div class=\"dc-label\">Design Choice #1<\/div>\n<p>Every block is an independently scalable Kubernetes deployment \u2014 so we can right-size each layer against its actual binding constraint rather than the largest one.<\/p>\n<\/p><\/div>\n<div class=\"design-card\">\n<div class=\"dc-label\">Design Choice #2<\/div>\n<p>The voice-agent service is co-located with the SFU in each region. Section 05 shows why this is the single largest user-perceived latency lever in the entire system.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>03<\/span> <\/div>\n<\/div>\n<p><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"><svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg><\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/div><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/div><\/div>\n<div class=\"page-body\">\n<div id=\"s02\" class=\"section-label\">Section 02<\/div>\n<h2 class=\"section-title\">How We Benchmarked<\/h2>\n<p class=\"body-text\">All numbers in this post come from controlled, repeatable load tests against live SimplAI infrastructure \u2014 not vendor marketing material. We deliberately mixed test methodologies so each layer was measured both in isolation and as part of the end-to-end path.<\/p>\n<table class=\"method-table\">\n<thead>\n<tr>\n<th>Regions<\/th>\n<th>Load Generators<\/th>\n<th>Measurement<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\n<div class=\"mt-title\">Central-US<\/div>\n<div class=\"mt-sub\">Primary deployment<\/div>\n<\/td>\n<td>\n<div class=\"mt-title\">Synthetic 1:1 audio<\/div>\n<div class=\"mt-sub\">SIPp UAC<\/div>\n<div class=\"mt-sub\">Scripted barge-in client<\/div>\n<\/td>\n<td>\n<div class=\"mt-title\">Protocol timestamps<\/div>\n<div class=\"mt-sub\">(T0\u2013T5)<\/div>\n<div class=\"mt-sub\">RMS-energy first-byte detect<\/div>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<div class=\"mt-title\">Mumbai (APAC)<\/div>\n<div class=\"mt-sub\">Cross-region tests<\/div>\n<\/td>\n<td>\n<div class=\"mt-title\">OpenAI-compatible<\/div>\n<div class=\"mt-sub\">streaming \/chat\/completions<\/div>\n<div class=\"mt-sub\">at 5 \u2192 1,000 concurrency<\/div>\n<\/td>\n<td>\n<div class=\"mt-title\">VART, TTFT, barge-in<\/div>\n<div class=\"mt-sub\">distributions captured<\/div>\n<div class=\"mt-sub\">per-run, p50\/p95\/p99<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p class=\"body-text\" style=\"font-weight:700; font-size:12px; margin-bottom:8px;\">Methodology in detail<\/p>\n<p class=\"body-text\">SFU concurrency was driven with a synthetic 1:1 audio load generator ramping from 100 \u2192 1,000 rooms in 5 rooms\/sec increments, holding each step for 3\u20135 minutes. SIP inbound was driven with SIPp as a UAC, issuing standard INVITE \u2192 200 OK \u2192 ACK \u2192 BYE flows at 5 calls\/second. Call-setup latency and Voice Agent Response Time were captured from a single client that timestamps each protocol event (T0\u2013T5) and detects the first audio byte via an RMS-energy threshold on the agent’s track. Agent-pipeline TTFT was measured by sending OpenAI-compatible streaming \/chat\/completions requests directly at each layer (LLM Router, agent-service end-to-end) at 5 \u2192 1,000 concurrency. Barge-in was measured with a scripted client that injects a 2-second interrupt utterance 2.0 s into the agent’s welcome and times the silence-and-recovery sequence. STT and TTS layers were characterized using a combination of vendor-published benchmarks and field measurements from our deployment.<\/p>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>04<\/span> <\/div>\n<\/div>\n<p><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"><svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg><\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/div><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/div><\/div>\n<div class=\"page-body\">\n<div id=\"s03\" class=\"section-label\">Section 03<\/div>\n<h2 class=\"section-title\">The Media Plane Holds: SFU at 1,000 Rooms<\/h2>\n<p class=\"body-text\">The first question for any voice platform is whether the media plane survives. We pushed the LiveKit SFU from 100 to 1,000 concurrent 1:1 audio rooms, with HPA configured at 80% CPU target and replicas bounded between 2 and 10.<\/p>\n<table class=\"data-table\">\n<thead>\n<tr>\n<th>Concurrent Rooms<\/th>\n<th>Pods<\/th>\n<th>Success<\/th>\n<th>Packet Loss<\/th>\n<th>Hottest Pod CPU<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>100<\/td>\n<td>2<\/td>\n<td>100%<\/td>\n<td>0.000%<\/td>\n<td>625 m<\/td>\n<\/tr>\n<tr>\n<td>1,000<\/td>\n<td>3<\/td>\n<td>100%<\/td>\n<td>0.000%<\/td>\n<td>2,252 m of 4,000 m<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div class=\"info-box\">\n<div class=\"info-box-label\">What This Means<\/div>\n<p>HPA crossed from 2 to 3 pods around the 200-room threshold, then held at 3 pods all the way to 1,000 rooms. The hottest pod was at 56% of its CPU limit and 43% of memory at peak \u2014 meaningful headroom, no queuing in room creation, no observed packet loss.<\/p>\n<p> <\/p>\n<p>For 1:1 voice traffic, the SFU is not the bottleneck at the scales our customers need. Per-room CPU cost trends downward at higher concurrency (~10 m \u2192 ~7 m per room) as shared signaling and connection overhead amortizes. We have room to grow before we touch this layer.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>05<\/span> <\/div>\n<\/div>\n<p><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"><svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg><\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/div><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/div><\/div>\n<div class=\"page-body\">\n<div id=\"s04\" class=\"section-label\">Section 04<\/div>\n<h2 class=\"section-title\">SIP Inbound: The Worker Is the Limit<\/h2>\n<p class=\"body-text\">For our calling-agent customers, SIP capacity matters more than SFU capacity. We drove inbound calls at 5 calls\/sec and varied voice-agent worker resources to find the binding constraint. Every failed call we observed traced back to the agent worker \u2014 never the SIP server, never the SFU. LiveKit’s load-based scheduling rejects new INVITEs when all workers are above its load > 0.7 threshold and the SIP server returns a protocol-correct 486 Busy Here.<\/p>\n<table class=\"data-table\">\n<thead>\n<tr>\n<th>Worker Pod CPU<\/th>\n<th>Observed Capacity<\/th>\n<th>Notes<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>1 core<\/td>\n<td>~3 calls \/ pod<\/td>\n<td>Needs \u2265 2\u20133 GB RAM; OOMs at 1 GB<\/td>\n<\/tr>\n<tr>\n<td>2 cores<\/td>\n<td>~7 calls \/ pod<\/td>\n<td>Stable up to 10 calls \/ 2 replicas<\/td>\n<\/tr>\n<tr>\n<td>4 cores<\/td>\n<td>~12 calls \/ pod<\/td>\n<td>Linear with CPU; memory not constraining<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div class=\"callout\">\n<p>SIP capacity scales linearly with worker CPU. To grow inbound, we grow agent workers \u2014 and the Section 09 capacity plan does exactly that.<\/p>\n<div class=\"callout-attr\">\u2014 Voice Platform Engineering<\/div>\n<\/p><\/div>\n<p class=\"body-text\">The takeaway is simple: SIP capacity scales linearly with worker CPU. The SIP server, room admission, and SFU all stayed silent during stress.<\/p>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>06<\/span> <\/div>\n<\/div>\n<p><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"><svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg><\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/div><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/div><\/div>\n<div class=\"page-body\">\n<div id=\"s05\" class=\"section-label\">Section 05<\/div>\n<h2 class=\"section-title\">The Single Biggest Latency Lever: Same-Region<\/h2>\n<p class=\"body-text\">VART \u2014 Voice Agent Response Time, from user end-of-speech to first byte of agent audio \u2014 is the metric users actually feel. We measured it in two topologies: voice-agent service and SFU in the same region, vs. across regions. <strong>All VART numbers in this post come from real-world scenarios we benchmark against \u2014 not curated demo runs.<\/strong> They are designed to mirror the conditions our enterprise customers actually run in, including realistic concurrency, prompt complexity, and network paths.<\/p>\n<table class=\"data-table\">\n<thead>\n<tr>\n<th>Metric<\/th>\n<th>Cross-region<\/th>\n<th>Same-region<\/th>\n<th>Improvement<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Welcome latency (room \u2192 first byte)<\/td>\n<td>3.76 s<\/td>\n<td>0.56 s<\/td>\n<td>\u221285%<\/td>\n<\/tr>\n<tr>\n<td>VART (end-of-speech \u2192 first reply byte)<\/td>\n<td>3.70 s<\/td>\n<td>1.80 s<\/td>\n<td>\u221251%<\/td>\n<\/tr>\n<tr>\n<td>Agent join time<\/td>\n<td>1.126 s<\/td>\n<td>0.002 s<\/td>\n<td>\u221299.8%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div class=\"callout\">\n<p>“Regional topology \u2014 not per-service resourcing \u2014 is the largest single user-perceived latency lever in the entire stack.”<\/p>\n<div class=\"callout-attr\">\u2014 SimplAI Voice Platform team<\/div>\n<\/p><\/div>\n<p class=\"body-text\">Several stages in the welcome and VART paths involve round-trips between the agent worker and the SFU. Each round-trip compounds inter-region RTT; co-locating the worker collapses the dispatch overhead from over a second to ~2 ms. Connection time remains ~3.0 s either way \u2014 that one is bounded by client-to-SFU transport, not by where we deploy the worker.<\/p>\n<p class=\"body-text\" style=\"font-weight:700; font-size:12px; margin-bottom:8px;\">The < 2-second VART target, budgeted<\/p>\n<p class=\"body-text\">We size each layer against a less than 2-second VART target, measured under real-world traffic \u2014 not demo conditions. Here’s how the budget breaks down across stages, anchored on the vendor benchmarks in Section 06 and the layer measurements in Section 07.<\/p>\n<div class=\"budget-bar-wrap\">\n<div class=\"budget-label\"> <span>Budget Breakdown \u2014 Under 2-Second VART Target<\/span> <span>1800 ms total<\/span> <\/div>\n<div class=\"budget-bar\">\n<div class=\"budget-seg b1\">150 ms<\/div>\n<div class=\"budget-seg b2\">250 ms<\/div>\n<div class=\"budget-seg b3\">1000 ms<\/div>\n<div class=\"budget-seg b4\">400 ms<\/div>\n<\/p><\/div>\n<div class=\"budget-labels\"> <span class=\"bl1\">WEBRTC<\/span> <span class=\"bl2\">STT<\/span> <span class=\"bl3\">LLM ROUTER<\/span> <span class=\"bl4\">TTS<\/span> <\/div>\n<div class=\"budget-axis\"> <span>0 ms<\/span> <span>1800 ms<\/span> <\/div>\n<div class=\"budget-caption\">Same-region VART of 1.80 s \u2014 measured against real-world scenarios, not demo conditions \u2014 sits inside this < 2 s target with ~200 ms of headroom. Driving it further toward sub-second is a continuous engineering investment \u2014 see Section 10.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>07<\/span> <\/div>\n<\/div>\n<p><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"><svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg><\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/div><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/div><\/div>\n<div class=\"page-body\">\n<div id=\"s06\" class=\"section-label\">Section 06<\/div>\n<h2 class=\"section-title\">Picking the Right Speech Vendors<\/h2>\n<p class=\"body-text\">STT and TTS are the two layers we don’t build ourselves. We bias toward vendors that win on the combination of latency, accuracy, and conversational features (turn-taking, diarization), then we measure them in our own pipeline rather than trusting the marketing page.<\/p>\n<p class=\"body-text\" style=\"font-weight:700; font-size:12px; margin-bottom:8px;\">STT \u2014 Deepgram Nova-3 \/ Flux<\/p>\n<p class=\"body-text\">For speech-to-text, what matters for VART is finalize latency: the delay between the user finishing their sentence and the complete transcript reaching the LLM. Streaming partials don’t help if the finalize stalls. Deepgram Flux is purpose-built for voice agents, with native end-of-turn detection that makes turn-taking feel natural.<\/p>\n<table class=\"data-table\">\n<thead>\n<tr>\n<th>Metric<\/th>\n<th>Deepgram Nova-3<\/th>\n<th>Deepgram Flux [In Use]<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Streaming latency (P50)<\/td>\n<td>~300 ms<\/td>\n<td>sub-300 ms<\/td>\n<\/tr>\n<tr>\n<td>WER (English, vendor)<\/td>\n<td>5.3% batch \/ 6.8% streaming<\/td>\n<td>Nova-3 quality level<\/td>\n<\/tr>\n<tr>\n<td>Speaker diarization<\/td>\n<td>Yes<\/td>\n<td>Yes<\/td>\n<\/tr>\n<tr>\n<td>Native end-of-turn detection<\/td>\n<td>\u2014<\/td>\n<td>Yes<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p class=\"body-text\" style=\"font-weight:700; font-size:12px; margin-bottom:8px;\">TTS \u2014 ElevenLabs Flash v2.5<\/p>\n<p class=\"body-text\">For text-to-speech, the metric is FTTS \u2014 first-byte time from the LLM’s first token to the first audio sample. Equally important is voice naturalness (MOS). Flash v2.5 wins both axes for our use cases. The one caveat is region proximity: we route Mumbai traffic through the US endpoint today, which adds ~250 ms of network latency on top of the ~200 ms model-inference TTFB.<\/p>\n<table class=\"data-table\">\n<thead>\n<tr>\n<th>Metric<\/th>\n<th>ElevenLabs Flash v2.5 [In Use]<\/th>\n<th>Deepgram Aura-2<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>TTFB \u2014 model inference<\/td>\n<td>~200 ms<\/td>\n<td>~250 ms<\/td>\n<\/tr>\n<tr>\n<td>TTFB \u2014 India via US endpoint<\/td>\n<td>~478 ms<\/td>\n<td>~250 ms<\/td>\n<\/tr>\n<tr>\n<td>MOS quality<\/td>\n<td>4.5\u20134.8 (human-like)<\/td>\n<td>Not published<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p class=\"body-text\" style=\"font-style:italic; font-size:9.5px; color: var(--text-muted);\">When ElevenLabs APAC GA lands, the Mumbai VART profile improves automatically. If the gap persists for any specific deployment, Deepgram Aura-2 is our pre-validated fallback for APAC traffic.<\/p>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>08<\/span> <\/div>\n<\/div>\n<p><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"><svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg><\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/div><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/div><\/div>\n<div class=\"page-body\">\n<div id=\"s07\" class=\"section-label\">Section 07<\/div>\n<h2 class=\"section-title\">Inside the Hot Path: Agent Orchestration under Load<\/h2>\n<p class=\"body-text\">The most interesting bottlenecks aren’t where you’d expect. The SFU is fine. The LLM provider is fine. The compression happens inside the two services that sit between them. The Agent-Service runs the agentic orchestration: conversation state, tool calls, traces, the loop that turns a transcript into the next agent action. The LLM calls from the Agent-Service then flow through the LLM Router, which combines model routing with our Model Aggregator \u2014 a thin service that manages provider API keys and transforms requests into a uniform OpenAI-compatible format.<\/p>\n<div class=\"stat-row\">\n<div class=\"stat-box s1\">\n<div class=\"stat-label\">Median TTFT @ 100 Concurrent<\/div>\n<div class=\"stat-value\">1.20 s<\/div>\n<div class=\"stat-sub\">P95 1.5 s \u00b7 0 failures<\/div>\n<\/p><\/div>\n<div class=\"stat-box s2\">\n<div class=\"stat-label\">Median TTFT @ 1,000 Concurrent<\/div>\n<div class=\"stat-value\">1.65 s<\/div>\n<div class=\"stat-sub\">P95 1.95 s \u00b7 0 failures \u00b7 40.7 calls\/s<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<p class=\"body-text\">With LLM Router pods and Agent Orchestrator pods sized appropriately, we sustain 1,000 concurrent calls across 3,000 requests with zero failures at 40.7 calls\/sec. The LLM provider itself contributes only a small fraction of that median; the bulk is orchestration overhead \u2014 routing, key management, request transformation, and the agentic loop.<\/p>\n<p class=\"body-text\" style=\"font-weight:700; font-size:12px; margin-bottom:8px;\">The lesson we learned the hard way: pod sizing matters<\/p>\n<p class=\"body-text\">The first time we ran the Model Aggregator portion of the LLM Router at 1,000 concurrency on its original sizing (1 core \/ 1.5 GB \u00d7 3 pods), it broke spectacularly: 33.75% failure rate, two pods restarting under resource exhaustion, a long tail well above 30 s. Doubling each pod to 2 cores \/ 3 GB resolved it cleanly. Same code, same traffic, completely different outcome.<\/p>\n<table class=\"data-table\">\n<thead>\n<tr>\n<th>Concurrency<\/th>\n<th>1 core \u00d7 3 pods<\/th>\n<th>2 cores \u00d7 3 pods<\/th>\n<th>Outcome<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>200<\/td>\n<td>stable, slow<\/td>\n<td>\u221244% mean TTFT<\/td>\n<td>Both stable<\/td>\n<\/tr>\n<tr>\n<td>500<\/td>\n<td>stable, slow<\/td>\n<td>\u221238% mean TTFT<\/td>\n<td>Both stable<\/td>\n<\/tr>\n<tr>\n<td>1,000<\/td>\n<td>33.75% failures \u00b7 pod restarts<\/td>\n<td>0 failures \u00b7 +138% throughput<\/td>\n<td>Clean win<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div class=\"highlight-box\">\n<div class=\"hl-label\">Where the Latency Actually Lives<\/div>\n<p>At 100 concurrency, the LLM provider contributes ~36% of the 1.20 s median TTFT (~430 ms). The remaining ~64% (~770 ms) is orchestration overhead from the agentic loop, model routing, and request transformation through the Model Aggregator. That’s good news: the overhead sits in our code, on our infrastructure, which means we can keep cutting it.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>09<\/span> <\/div>\n<\/div>\n<p><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"><svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg><\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/div><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/div><\/div>\n<div class=\"page-body\">\n<div id=\"s08\" class=\"section-label\">Section 08<\/div>\n<h2 class=\"section-title\">Naturalness: Barge-In and Recovery<\/h2>\n<p class=\"body-text\">Latency makes voice agents fast. <strong>Barge-in is what makes them feel human.<\/strong> If a caller starts speaking while the agent is mid-sentence, a natural agent stops quickly, listens, and responds with a new answer. We measured this end-to-end across 20 sequential runs on our production stack.<\/p>\n<div class=\"stat-row\">\n<div class=\"stat-box s1\">\n<div class=\"stat-label\">Barge-In Time P50<\/div>\n<div class=\"stat-value\">0.60 s<\/div>\n<div class=\"stat-sub\">p95 0.63 s \u00b7 range 203 ms<\/div>\n<\/p><\/div>\n<div class=\"stat-box s2\">\n<div class=\"stat-label\">Recovery Time P50<\/div>\n<div class=\"stat-value\">1.82 s<\/div>\n<div class=\"stat-sub\">STT \u2192 LLM \u2192 TTS path<\/div>\n<\/p><\/div>\n<div class=\"stat-box s3\">\n<div class=\"stat-label\">Welcome P50<\/div>\n<div class=\"stat-value\">0.45 s<\/div>\n<div class=\"stat-sub\">First audio byte<\/div>\n<\/p><\/div>\n<div class=\"stat-box s4\">\n<div class=\"stat-label\">Consistency<\/div>\n<div class=\"stat-value\">\u00b131 ms<\/div>\n<div class=\"stat-sub\">p95 vs p50 spread<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<p class=\"body-text\">The total spread across 20 runs was just 203 ms \u2014 p95 sat 31 ms above p50, p99 within 88 ms \u2014 pointing to a well-tuned VAD and TTS-cancellation pipeline with no significant tail. The barge-in pipeline includes a 300-ms silence-confirmation window that’s deliberately conservative; the underlying audio cut-off reaction is closer to 250\u2013400 ms.<\/p>\n<p class=\"body-text\">Recovery shares the same STT \u2192 LLM \u2192 TTS path as a normal turn, so its variance tracks the agent-pipeline characteristics in Section 07 \u2014 the cleaner we make the agent orchestration and LLM Router, the cleaner barge-in recovery feels.<\/p>\n<div class=\"highlight-box\">\n<div class=\"hl-label\">Why This Matters for Naturalness<\/div>\n<p>The hardest UX failure mode in voice agents is the agent talking over the user. A consistent ~600 ms cut-off \u2014 measured across 20 runs with a 31 ms p95\u2013p50 spread \u2014 is what lets a SimplAI voice agent feel like a person who’s actually listening, not a TTS engine that won’t stop.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>10<\/span> <\/div>\n<\/div>\n<p><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"><svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg><\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/div><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/div><\/div>\n<div class=\"page-body\">\n<div id=\"s09\" class=\"section-label\">Section 09<\/div>\n<h2 class=\"section-title\">Capacity Plan: 1,000 Concurrent Calls<\/h2>\n<p class=\"body-text\">Putting it all together, here’s what it takes to run 1,000 concurrent voice conversations on the SimplAI stack \u2014 sized from the actual measurements above, with realistic headroom.<\/p>\n<table class=\"data-table\">\n<thead>\n<tr>\n<th>Layer<\/th>\n<th>Pods<\/th>\n<th>Per-pod Resources<\/th>\n<th>Notes<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>LiveKit SFU<\/td>\n<td>3 \u2013 5<\/td>\n<td>1 c req \/ 4 c limit \u00b7 4 GB<\/td>\n<td>HPA at 80% CPU; 3 pods at 1,000 rooms with burst headroom<\/td>\n<\/tr>\n<tr>\n<td>Voice-Agent Worker<\/td>\n<td>~85<\/td>\n<td>4 cores \u00b7 3 GB<\/td>\n<td>~12 calls\/pod; binding constraint for SIP. WebRTC scales similarly<\/td>\n<\/tr>\n<tr>\n<td>LLM Router<\/td>\n<td>3 \u2013 4<\/td>\n<td>2 cores \u00b7 3 GB<\/td>\n<td>Sustained 1,000 conc. with zero failures at this sizing<\/td>\n<\/tr>\n<tr>\n<td>Agent-Service<\/td>\n<td>3 \u2013 5<\/td>\n<td>3 cores \u00b7 5 GB<\/td>\n<td>I\/O-bound; throughput scales near-linearly with replicas<\/td>\n<\/tr>\n<tr>\n<td>STT (Deepgram)<\/td>\n<td>SaaS<\/td>\n<td>Concurrency quota<\/td>\n<td>Pre-provisioned per region with vendor<\/td>\n<\/tr>\n<tr>\n<td>TTS (ElevenLabs)<\/td>\n<td>SaaS<\/td>\n<td>Concurrency quota<\/td>\n<td>APAC routing fallback ready<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p class=\"body-text\" style=\"font-style:italic; font-size:9.5px; color: var(--text-muted);\">Numbers above assume same-region deployment per our default topology. Voice-agent worker count dominates the cost line \u2014 and is the layer most amenable to next-quarter optimization (see Section 10).<\/p>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>11<\/span> <\/div>\n<\/div>\n<p><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"><svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg><\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/div><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/div><\/div>\n<div class=\"page-body\">\n<div id=\"s10\" class=\"section-label\">Section 10<\/div>\n<h2 class=\"section-title\">What We Ship Next<\/h2>\n<p class=\"body-text\">Numbers like these are a checkpoint, not a finish line. The road map for the next two quarters keeps the same north stars \u2014 sub-second VART, human-feel barge-in, no tail failures at 1,000+ concurrent \u2014 and pushes them further.<\/p>\n<div class=\"roadmap-grid\">\n<div class=\"roadmap-card\">\n<div class=\"rc-num\">01<\/div>\n<div class=\"rc-title\">Drive LLM Router TTFT under 500 ms at 1,000 concurrent<\/div>\n<p>Most of the remaining latency in the LLM Router is orchestration overhead \u2014 model resolution, routing, request shaping. We’re refactoring the hot path with prefetched config, fewer cross-service hops, and aggressive connection pooling. Target: median TTFT < 500 ms, P95 < 1 s, at 1,000 concurrent.<\/p>\n<\/p><\/div>\n<div class=\"roadmap-card\">\n<div class=\"rc-num\">02<\/div>\n<div class=\"rc-title\">Higher density per voice-agent worker<\/div>\n<p>Today’s ~12 calls\/pod at 4 cores is a clean linear-scaling number, but it’s also the dominant cost line at 1,000 concurrency. Profiling work on the worker hot loop \u2014 reducing per-call goroutine cost, batching control-plane messaging \u2014 targets ~20\u201325 calls\/pod in the next release.<\/p>\n<\/p><\/div>\n<div class=\"roadmap-card\">\n<div class=\"rc-num\">03<\/div>\n<div class=\"rc-title\">Regional autonomy for STT and TTS<\/div>\n<p>The Mumbai TTS gap is the only place where geography still costs us. We’re piloting ElevenLabs APAC as it rolls into GA, plus a per-tenant policy that auto-fails over to Deepgram Aura-2 when network TTFB exceeds threshold. The goal is same VART profile in APAC as in NA.<\/p>\n<\/p><\/div>\n<div class=\"roadmap-card\">\n<div class=\"rc-num\">04<\/div>\n<div class=\"rc-title\">Tighter barge-in<\/div>\n<p>Today’s 0.6 s p50 is in the “Good” band; sub-300 ms is what we want \u2014 the “feels like interrupting a human” range. We’re lowering the silence-confirmation window where signal quality allows, and exploring on-device VAD to skip a network hop on the cancel path.<\/p>\n<\/p><\/div>\n<div class=\"roadmap-card full\">\n<div class=\"rc-num\">05<\/div>\n<div class=\"rc-title\">Continuous load and barge-in regression suite<\/div>\n<p>Every benchmark above is being lifted into our CI: a weekly regression run that drives 100 \u2192 1,000 concurrent calls, captures VART and barge-in distributions, and gates production deploys on no regression vs. the previous baseline. Performance becomes a property we ship, not an audit we run.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>12<\/span> <\/div>\n<\/div>\n<p><\/p>\n<div class=\"page\">\n<div class=\"page-header\">\n<div class=\"brand\">\n<div class=\"logo-icon\"><svg viewBox=\"0 0 24 24\"><path d=\"M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 14H9V8h2v8zm4 0h-2V8h2v8z\"\/><\/svg><\/div>\n<div>\n<div class=\"brand-name\">SimplAI<\/div>\n<div class=\"brand-sub\">Making AI Simple<\/div>\n<\/div><\/div>\n<div class=\"header-title\">Voice Agents That Don’t Break at Enterprise Scale<\/div>\n<div class=\"header-right\">\n<div class=\"header-tag\">Engineering \u00b7 Voice AI \u00b7 April 2026<\/div>\n<\/div><\/div>\n<div class=\"page-body\">\n<div id=\"s11\" class=\"section-label\">Section 11<\/div>\n<h2 class=\"section-title\">The SimplAI Commitment<\/h2>\n<p class=\"commitment-intro\">Voice is the highest-bar interface software has \u2014 there’s nowhere to hide a 4-second pause or a robotic interruption. The stack we’ve built holds 1,000 concurrent calls with zero packet loss, hits a 1.80-second VART end-to-end against real-world benchmarked scenarios (not demos), and lets the agent stop talking in 600 ms when a caller chimes in. Whether you’re deploying outbound calling agents at scale or embedding a conversational agent in your product, the same infrastructure carries the call.<\/p>\n<p class=\"commitment-intro\">We’re committed to keeping that bar moving. The benchmarks in this post are public so customers, partners, and our own teams can hold us to them. The next set of numbers ship with the next release.<\/p>\n<div class=\"cta-block\">\n<div class=\"cta-label\">Get in Touch<\/div>\n<h2>Want to see SimplAI Voice in your stack?<\/h2>\n<p>Talk to our team about same-region deployment, your concurrency targets, and your VART budget. We’ll size it together.<\/p>\n<p> <a href=\"https:\/\/simplai.ai\/request-demo\" class=\"cta-btn\" target=\"_blank\" rel=\"noopener\">TALK TO THE TEAM \u2192<\/a> <\/div>\n<\/p><\/div>\n<div class=\"page-footer\"> <span>simplai.ai \u00b7 Voice Platform<\/span> <span>Voice Agents at Enterprise Scale \u00b7 April 2026<\/span> <span>13<\/span> <\/div>\n<\/div>\n<p><\/body><\/html><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Voice Agents That Don’t Break at Enterprise Scale \u2014 SimplAI SimplAI Making AI Simple Voice Agents That Don’t Break at Enterprise Scale Engineering \u00b7 Voice…<\/p>\n","protected":false},"author":1,"featured_media":5523,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-3700","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technical-insights"],"yoast_head":"\n<title>Voice Agents That Do not Break 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