Voice AI Bot Automates 70% of Inbound Calls

Customers waste time in queues, and operators burn out on repetitive questions. 35% of calls are about order status; 25% are FAQs. Each such call lasts 3–5 minutes, with 2 minutes spent on identification and data lookup. Operators spend up to 70% of their time on repeated requests, leading to high turnover and rising costs. We automate inbound call processing using a voice chatbot built on ASR (Yandex SpeechKit, Silero) → LLM (GPT-4o-mini, LLaMA 3) → TTS. The bot understands natural speech, extracts intent from the first sentence, and resolves 60–75% of tasks without escalation. The target containment rate is 70%, and for typical requests it reaches up to 95%. Our team has 5 years of experience in voice AI solutions and certifications on leading platforms. According to internal tests, fine-tuning on 500 examples increases containment rate by 15%. This solution reduces call center costs by up to 50%, saving $200,000 annually for a mid-size call center. It is 2 times more effective than traditional IVR and 3 times faster in resolving simple requests.

Typology of Inbound Inquiries

How does the AI bot handle complex requests?

For compound requests, the bot uses a chain of intents. Context is stored in dialog memory based on DialogScenario. If the model is unsure, it asks a clarifying question rather than escalating immediately. This allows automation of 40% of inquiries that seem complex. For example, a customer asks "when will my parcel arrive and can I change the address?" — the bot uses RAG for calls to retrieve the order number from CRM, offers delivery options, and in 80% of cases the customer confirms the solution without transfer to an operator.

Why is context transfer to the operator critical?

Upon escalation, the bot passes structured context: intent, extracted entities, and a 2–3 sentence dialog summary. The operator sees this before connecting and does not need to ask again — call time is reduced by 50%. We use the AGENT_BRIEFING_TEMPLATE for formatting. This reduces operator load and speeds up problem resolution.

How we implement the voice AI bot

We build the bot on the ASR → LLM → TTS stack. For speech recognition we use Yandex SpeechKit or Silero, for response generation — GPT-4o-mini or LLaMA 3, for synthesis — the same TTS stack. Scenarios are described as graphs with transitions by intent and entities. Our speech analytics module monitors sentiment to adapt responses in real time.

Multi-scenario routing

from typing import Callable @dataclass class DialogScenario: name: str triggers: list[str] handler: Callable priority: int = 0 SCENARIOS = [ DialogScenario( name="order_status", triggers=["order", "status", "where is my parcel", "when will it be delivered"], handler=handle_order_status_scenario, priority=10 ), DialogScenario( name="account_balance", triggers=["balance", "remaining", "how much money", "account"], handler=handle_balance_scenario, priority=10 ), DialogScenario( name="technical_issue", triggers=["not working", "error", "broken", "problem"], handler=handle_tech_support_scenario, priority=5 ), ] async def route_to_scenario(user_text: str) -> DialogScenario: response = await client.chat.completions.create( model="gpt-4o-mini", messages=[{ "role": "system", "content": f"Determine the scenario from: {[s.name for s in SCENARIOS]}. JSON: {{'scenario': '...'}}" }, {"role": "user", "content": user_text}], response_format={"type": "json_object"} ) scenario_name = json.loads(response.choices[0].message.content)["scenario"] return next((s for s in SCENARIOS if s.name == scenario_name), SCENARIOS[-1]) 

Context transfer to operator

async def transfer_to_agent(session: CallSession, reason: str): context = { "call_id": session.call_id, "phone": session.phone, "customer": await lookup_customer(session.phone), "dialog_summary": await summarize_dialog(session.history), "intent": session.current_intent, "escalation_reason": reason, "timestamp": datetime.utcnow().isoformat() } await crm.create_case(context) await telephony.transfer_call(session.call_id, agent_queue="support") 

Operator notification before connection

AGENT_BRIEFING_TEMPLATE = """ Transferring client {phone}. Reason for contact: {intent}. Client has already reported: {summary}. No need to ask: order number and name — already obtained. """ 

AI bot vs traditional IVR

This AI bot resolves 2 times more inquiries than IVR and reduces call center costs up to 50%. We guarantee containment rate of at least 60% after two weeks of pilot.

What's included in the work

We provide the full development cycle:

• Audit of current calls and identification of typical requests
• Writing dialog scenarios for each type of request
• Integration with telephony (Asterisk, MTT, Rostelecom) and CRM (AmoCRM, Bitrix24)
• Model training on your data (fine-tuning, few-shot)
• Testing on pilot traffic (minimum 500 calls)
• Documentation of scenarios and API
• Training operators on working with the bot
• Support and refinement after launch

We guarantee quality: after implementation we conduct an audit and adjust scenarios to achieve the target containment rate.

Implementation process

1. Analytics — analysis of call recordings, identification of typical scenarios
2. Design — prototype scenarios on the chosen stack
3. Development — scenario implementation, CRM integration, escalation setup
4. Testing — A/B test with a pilot group, scenario adjustment
5. Deployment — launch on production traffic, p99 latency monitoring

Timeline

A single scenario (e.g., order status) takes from 2 weeks. A full multi-scenario bot with integration takes from 2 to 3 months. Timeline is determined after an audit.

Want to see the result on your own calls? Get a consultation: we will analyze 50–100 recordings, prepare a scenario prototype, and show the containment rate on your data. Order a pilot — 500 calls in test mode. Contact us for a free audit. See the effectiveness of the AI bot in practice.