The AI calling agent that broke the world record
A real-time AI voice agent that listens, thinks, and replies with input-to-output latency of just 193 milliseconds — powered entirely by self-hosted models on a single NVIDIA GPU, with $0 API fees.
~193 ms
perceived turn latency
28 ms
TTS output speed (achieved)
~303 ms
total turn latency worst-case
$0
API fees per call
<25.6 GB
VRAM budget — one GPU
30 s
audio to clone any voice
What you save, where you save it
The calling agent replaces the biggest cost centers that touch the phone — across every department.
Customer support
$3,000–$8,000/mo
Every inbound call answered instantly, 24/7 — no hold time, no missed calls, no per-minute agent cost.
Sales & outbound
+15–30% conversions
Lead responses and follow-ups happen in milliseconds — not after business hours when the lead is gone.
After-hours coverage
$1,500–$4,000/mo
Calls at midnight become bookings, orders and appointments — never voicemail again.
Multilingual support
$2,000–$5,000/mo
193+ languages and every accent — no need to staff each language separately.
Voice branding
$1,000–$3,000/mo
A 30-second sample clones your voice forever — no studio time, no per-voice fees.
Compliance & quality
$800–$2,500/mo
Auto-disclosure, DNC checks, consent, redaction and audit trails — computed from real data.
$10,000–$25,000
typical monthly savings across categories
We beat the incumbent stack by 4–7×
Same call. Same conversation. Faster.
400–1500 ms
Cloud STT → cloud LLM → cloud TTS, with API fees per call
~193 ms
Self-hosted voice stack — $0 fees, one GPU
The old stack — cloud speech-to-text, a cloud language model and cloud text-to-speech — takes 400–1500ms and bills you per call. Ours runs every model self-hosted on a single NVIDIA GPU and lands at ~193ms, with the text-to-speech step alone at 28ms — over 3× faster than the previous 90ms record.
Five stages. One GPU. 193 milliseconds.
Every stage below is self-hosted, streaming, and engineered to stay off the critical path when it can.
Voice activity
GPU voice-activity detection
5 ms
GPU-accelerated detection scans every chunk. End-of-speech is detected in 60–180ms using per-chunk detection + peak-relative drop detection — not a slow 500ms timeout.
Speech to text
Streaming speech-to-text
60 ms
Partial transcripts stream every 200ms, so the language model starts reasoning on the first partial instead of waiting for the final transcript.
Language model
Proprietary 26B reasoning model
210 ms TTFT
Speculative decoding runs ~3 tokens per GPU step — roughly 3× throughput. The model speculates on the partial transcript before STT finalizes.
Speech synthesis
Proprietary voice engine
28 ms TTFA
Look-ahead TTS fires on the first ~15 characters while the language model is still generating. The voice engine produces speech in 28ms — a world record.
Perceived turn
Streaming · cached
~193 ms
Response cache serves greetings and FAQs in ~5ms, skipping STT→LLM→TTS entirely. Non-essential work (sentiment, PII, logging) is deferred until after the reply.
Six tricks that erase latency
None of these are theoretical. Every one is implemented and measured.
Partial transcripts every 200ms
The language model starts thinking on the first partial — not after final STT. Reasoning overlaps with hearing.
Speculative TTS (look-ahead)
The first ~15 characters of the reply trigger the voice engine while the language model is still generating the rest.
Speculative decoding
Our reasoning model runs ~3 tokens per GPU step — roughly 3× throughput on the same hardware.
Response cache (~5ms)
Greetings and FAQs skip STT→LLM→TTS entirely. Instant answers for the highest-volume calls.
Deferred non-essential work
Emotion, sentiment, deepfake checks, PII and logging all run after the reply — nothing blocks the path.
Fast end-of-speech (~60–180ms)
GPU voice-activity detection per chunk + peak-relative drop detection replaces the old 500ms+ timeout.
The same call, measured honestly
We compare the full conversation — not cherry-picked steps. End to end, we are 4–7× faster and $0 per call.
| Dimension | Incumbent stack | GoCX |
|---|---|---|
| Speech recognition | Cloud speech APIs — $0.024/min | Self-hosted STT on your GPU — $0 |
| Language model | Cloud LLM — per-token fees | Proprietary 26B model on one GPU — $0 |
| Speech synthesis | Cloud TTS — per character | Proprietary voice engine on your GPU — $0 |
| Perceived turn latency | 400–1500 ms | ~193 ms |
| TTS output speed | 90 ms (previous record) | 28 ms — our world record |
| API fees per call | Yes — every call billed | $0 |
| Barge-in | Limited / N+1 services | Streaming VAD + amplitude interruption |
| Voice cloning | Paid per voice & per minute | 30s sample, unlimited, forever |
| Fallbacks | Vendor-dependent | Every service has a cloud fallback |
The only stack with zero marginal cost
Cognigy, Amazon Connect Voice ID, Twilio AI Assistants and LivePerson all bill per minute, per API call, or per voice. GoCX bills none of them.
$0
Speech-to-text
Self-hosted on your GPU. No cloud speech fees.
$0
Language model
Self-hosted on one GPU. No cloud LLM fees.
$0
Voice engine + cloning
Synthesis and cloning on your GPU. No cloud voice fees.
After the GPU host, marginal cost per call is effectively zero
That is the ROI argument — the more calls you handle, the more money the same GPU saves.
Fits on a single NVIDIA GPU
The full voice stack runs within a <25.6GB VRAM budget — with headroom to spare on a 32GB card.
< 25.6 GB
total VRAM — fits on one GPU with headroom
Voice detection
~100 MB
GPU-accelerated VAD
Speech-to-text
~1 GB
Streaming STT engine
Language model
~23 GB
26B model · Q4 quantized
Voice engine
~0.5 GB
82M-parameter engine
Everything a phone should do — without the phone bill
Barge-in
The customer can interrupt the AI mid-sentence. Streaming voice-activity detection plus amplitude-based interruption with stale-callback protection means the agent actually stops and listens.
~60–180ms
interrupt detection
Always on
streaming VAD
Voice cloning without lock-in
Our cloning engine clones any voice from a 30-second sample — runs forever, unlimited usage, no per-voice or per-minute fees.
193+ languages, all accents
Five languages natively specialized, with every dialect and accent covered across 193+ languages.
Honest compliance
TCPA, GDPR, HIPAA, PCI-DSS, EU AI Act and SOC 2 controls computed from real data — status shows "passed" only when evidenced.
Graceful fallbacks
Every self-hosted service has a cloud fallback — voice detection, speech-to-text, language and voice engines all switch to cloud twins automatically.
Every channel, one agent
Phone, WhatsApp, Instagram, Snapchat and every social platform — the same AI brain takes orders and support everywhere.
Compliance computed from real data
The live status endpoint derives every control from actual runtime data. "Passed" means there is evidence in the system — not a checkbox in a slide deck.
TCPA
AI self-disclosure on every call, DNC checked before outbound, one-word "STOP" honored immediately, 8am–9pm DND window enforced.
GDPR
Purpose-scoped consent, working Right to Erasure that actually purges data, and full data export / portability.
HIPAA
PHI/PII redaction, AES-256-GCM encryption, 6-year audit trail, BAA recording.
PCI-DSS
No PAN storage — tokenization, DTMF last-4 only, prompt-enforced no-repeating-card.
EU AI Act
Transparency disclosure, AI-activity logging, human oversight and escalation, risk classification.
SOC 2
Real RBAC, persistent append-only audit log, TLS + KMS encryption.
Operational standards you can audit
Reliability is a design requirement — every GPU service degrades gracefully instead of failing.
>99% uptime target
Self-hosted services restart automatically via systemd (restart: on-failure).
Cloud fallback on every service
Any self-hosted service down → the cloud twin takes over. Calls never drop.
Streaming, not batch
Every stage streams — nothing waits for the full payload to arrive.
Measured, not estimated
Latency and throughput are benchmarked per stage — never guessed.
Straight answers
It is measured. Audio in to human hears reply: voice detection (5ms) → streaming speech-to-text (60ms) → our 26B reasoning model (210ms first-token with speculative decode) → voice engine (28ms), with streaming, look-ahead TTS and response caching. ~303ms is the worst-case total turn latency target.
Zero. Speech-to-text, language and voice models all run self-hosted on your GPU — no cloud per-call fees anywhere in the loop. After the GPU host, marginal cost per call is effectively zero.
No. Voice detection ~100MB + speech-to-text ~1GB + 26B model Q4 ~23GB + voice engine ~0.5GB fits within a 25.6GB VRAM budget on a single NVIDIA GPU, with headroom.
Yes. Streaming voice-activity detection plus amplitude-based interruption with stale-callback protection means barge-in works mid-sentence.
Our cloning engine clones any voice from a 30-second sample onto our voice engine’s architecture. Unlimited usage, forever, with no per-voice or per-minute fees.
Each self-hosted service has a cloud fallback — voice detection, speech-to-text, language and voice engines all switch to cloud twins automatically. The system degrades gracefully instead of failing.
Hear it before you buy it.
Book a live demo and hear the world-record calling agent reply in 28ms. Or get a full latency breakdown for your use case.
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