Voice AIConversational SystemsASR · LLM · TTS

The most repetitive calls
do not have to reach the team.

We design voice assistants integrated with telephony, CRM, catalog data, and order systems. They need to answer quickly, speak in the customer’s language, and know when to pass the case to a person.

Book a discovery call 30 min
Live demo

Hear it handle a real call.

A sample call with a product assistant. Press play - transcript, tool calls, and analysis appear in real time.

Product assistant
Sample recording
0:00 / 0:40
Caller: Female · 92% model ↗ Purchase intent: High Upsell opportunity: Yes
Transcript
Hi, I’m looking for a warm ski jacket for my husband, for Christmas.
Hi! Happy to help. What size, and any colour in mind?
Size L, something dark ideally.
I’d suggest the AlpStorm in black - 20k membrane, very warm. It’s in stock in L.
Sounds good. Will it arrive before Christmas?
Yes - order today and it ships to arrive by Dec 23.
Goggles and gloves from the same line pair well with it - add them to the cart?
Just the goggles, please.
Added. I’ll summarise the order and send a confirmation.
What the AI does
search_catalogSearches catalog: men’s ski jackets
check_stockChecks stock: AlpStorm · L · black
delivery_estimateEstimates delivery: Dec 23
suggest_addonsSuggests add-ons: goggles, gloves
create_orderCreates order and confirmation
Call analysis
Call sentiment Positive
Upsell Offered goggles and gloves - goggles accepted
Resolution Yes - product chosen and in cart
Purchase intent High
Est. CSAT 4.7 / 5
PII redaction on calls PII Guard · open source ↗
Open stack · Production telephony

Built on open source.
Connected to your phone system.

We integrate with
We speak the protocols
SIPRTP / SRTPWebRTCSDPICE / STUN / TURNTLS

The economics - by the numbers

391%

ROI over 3 years documented for enterprise voice AI, with payback in roughly 6 months

Forrester / PolyAI TEI
80%+

Of inbound calls handled autonomously in mature deployments

Callbotics
5–10×

Typical Q4 call-volume spike that can be handled without seasonal hiring

Industry benchmark
<700ms

Response latency on our LiveKit deployments

Our production infrastructure
Why we build a custom pipeline

Off-the-shelf bots hit limits quickly.
A custom system can improve.

Calls contain product names, shortcuts, accents, and issues specific to your store. That is why architecture matters: speech recognition, conversation logic, voice synthesis, and integrations are controlled separately. Over time, the system can be tuned on real recordings instead of relying on a generic model.

Three connected modules in a pipeline - ASR, LLM, and TTS stages of a production voice AI system

ASR · LLM · TTS - tuned to your systems

Week 1
Baseline

Integration, call-flow design, and the first narrow use case. The assistant handles one call type from start to finish.

Week 8
Production

ASR is tuned on real recordings. Human handoff works, and KPIs are measured daily: resolution rate, handle time, and CSAT.

Month 6+
Expansion

The system understands more vocabulary, exceptions, and store-specific scenarios. New call types are added in phases, without rebuilding the architecture.

Where off-the-shelf voice bots usually break

Speech recognition

Accents, noise, and product names quickly expose the limits of generic models.

Integrations

Without CRM, ticketing, or OMS access, the assistant can do little more than take a message.

Languages

Many platforms are designed for English first. Polish and CEE languages need separate quality control.

Compliance

GDPR, HIPAA, and PCI-DSS are not add-ons. Architecture decides what is possible in regulated workflows.

Latency

A 1.5–3s delay is enough for the conversation to feel unnatural.

Pilot-to-production

A demo often works on a few ideal scenarios. Production starts with exceptions and real volume.

The service

Voice AI designed
for your processes.

Four layers that need to fit the real support workflow: conversations, data, telephony, and the team’s day-to-day work.

The pipeline
Core pipeline

ASR · LLM · TTS architecture

  • Speech recognition tuned to language, accents, and product names
  • LLM layer with access to catalog data, orders, and support policies
  • Voice synthesis matched to brand tone and call type
  • Realtime models configured for low latency
Integration

Integration with store systems

  • CRM, helpdesk, and order-management connections
  • SIP trunk and telephony infrastructure setup
  • WebRTC support for browser-based calls
  • API adapters for proprietary or closed backends
The operations layer
Handoff

Handoff and escalation

  • Escalation to a human with a short case summary
  • Call classification and routing logic
  • Automatic call summaries in CRM or helpdesk
  • Agent-assist mode where AI supports the consultant
Measurement

Analytics and ongoing improvement

  • Resolution rate, handle time, and CSAT tracking
  • Analysis of call topics and recurring problems
  • Conversation-flow tests based on real cases
  • Regular quality review and improvement plan

Related areas

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 SyntropicOmni

Every channel in one inbox, phone included

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 AI Visibility · GEO

Visibility in AI answers

Before an agent moves to checkout, AI has to find and understand the store's offer. ChatGPT, Gemini, Perplexity, and Google AI. →

 Agentic Commerce

Catalogs ready for shopping agents

Once AI finds your store, an agent still needs to understand products, variants, availability, and checkout. ACP, UCP, feeds, and structured data. →
Start the conversation

Let’s identify which calls
to automate first.

In 30 minutes we’ll review call types, volume, current support flow, and the systems that need to be connected. You’ll leave with an honest answer on whether Voice AI makes sense here.

Book a free discovery call

Or email hello@syntropicsignal.ai