Low-latency speech pipeline
Speech-to-text and text-to-speech tuned to keep responses feeling instant, not robotic.
AI · Case study
AI voice support agent
A real-time AI voice assistant that understands callers, answers instantly and hands off to humans — cutting wait times around the clock.
Industry
Customer support
Engagement
AI product
Stack
Python · LLMs · AWS
The challenge
Hold times were driving customers away.
Call volume spiked outside business hours and human agents couldn't keep pace, leaving callers stuck in queues for routine questions that took seconds to answer once someone finally picked up.
Every added language or shift only added more headcount and cost, not faster answers — and scripted IVR menus frustrated callers more than they helped, pushing many to hang up before reaching a person.
Real-time
What we built
A voice agent that listens, answers and escalates.
Support-grounded LLM
Trained on the client's own support content so answers are accurate and on-brand.
Seamless human handoff
Complex or sensitive calls route to a person with full context carried over.
Multilingual by design
Callers are understood and answered in their own language, automatically.
Live call analytics
Dashboards tracking intent, sentiment and resolution as calls happen.
Guardrails & escalation rules
Clear boundaries on what the agent can promise, with safe fallbacks built in.
How we delivered
From call transcripts to a live agent.
STEP 01
Discovery
Analyzed call transcripts to map intents, volume and escalation paths.
STEP 02
Design
Scripted conversation flows and defined clear escalation boundaries.
STEP 03
Build
Shipped the real-time speech pipeline and grounded LLM behind it.
STEP 04
Launch
Rolled out gradually across queues, watching live metrics closely.
STEP 05
Iterate
Retrained on new call patterns to keep accuracy climbing post-launch.
The results
Measurable impact.
The agent now handles the majority of routine calls end-to-end, freeing the human team for what actually needs them.
24/7
Coverage
-58%
Wait time
12
Languages
+40%
CSAT
Tech stack
Real-time voice, engineered for scale.
PythonLLMsAWSSpeech-to-textText-to-speechSIP/TelephonyVector DBsReal-time analytics
"Callers genuinely can't tell they're talking to an AI until we tell them — and our human agents now spend their time on the calls that actually need a person. LogicLabs nailed the handoff experience."
RT
R. Tran
Head of Support, customer support platform
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