Note: when incoming orders land in CRM, and confirmation hangs for hours, churn begins. We develop voice bots for automated order confirmation calls that intercept this moment: they call 30 minutes after order placement, 2 hours after non-payment.
Confirmation conversion is 70–80%, the non-purchase rate drops from 20% to 5–8%. Savings on the operator department reach 30–50% of current costs, and the average call cost is calculated after audit and is tens of times lower than a live operator.
Recently, for an online consumer electronics store with 8,000 orders per day, we implemented a bot: in a month, the non-purchase rate fell from 25% to 7%, and confirmation conversion reached 82%. The operator department was reduced by 40% based on project data. Contact us to discuss a similar case.
Problems that AI automated calling solves
The first is the human factor: operators spend time on repeats, forget to call back, make mistakes in statuses. The second is scaling: during peak season, 100+ orders/hour, hiring that many operators is unrealistic.
The third is handling cancellations: a customer may cancel an order or change the address — this needs to be processed correctly and passed to logistics.
In reaction speed, the AI bot is 20–40 times faster than an operator: the call is initiated in 5–15 seconds versus 5–10 minutes for a human. At the same time, the bot's logging accuracy is 100%, while an operator's is no more than 70%.
We designed a system that solves these problems without sacrificing quality. At its core is a dialogue engine based on LLM (GPT-4o, LLaMA 3, or Qwen — depending on latency requirements), which understands natural speech and flexibly reacts to deviations from the script. You can read more about LLM technology in reference materials.
How does the AI bot process an order cancellation?
When a cancellation signal is received, the system records the reason (code and comment), blocks the start of assembly, and initiates a refund (if prepayment was made). All changes are synchronized with CRM in real time via WebSocket.
If the customer cancels partially, the bot clarifies the items and creates a change order. Cancellation processing speed is less than a second, which is critical for logistics windows.
Why is a retry strategy critical for conversion?
Up to 30% of orders do not answer at the time of the first call. Without retry, these orders become non-purchases. Our algorithm uses an adaptive schedule for the retry strategy: first call after 30 minutes, second after 2 hours, third after 6 hours.
If we cannot reach them, we send an SMS with a confirmation URL and assign a task to an operator. This scheme increases the percentage of confirmed orders by 15–20%.
RETRY_SCHEDULE = [ timedelta(minutes=30), # first retry timedelta(hours=2), # second retry timedelta(hours=6), # third retry ] After three failed attempts — a push notification to the mobile app or an operator call.
How to implement AI automated calling in 3 steps
- Process audit — we analyze current scripts, triggers, and integrations (1–2 days).
- MVP launch — we implement a basic scenario with retry logic and integration via REST API (5–7 days).
- Optimization — based on data from the first 1,000 calls, we adjust scenarios and configure escalations (3–5 days).
Comparison: AI bot vs live operator
| Criterion | AI bot | Operator |
|---|---|---|
| Call speed | 5–15 sec after trigger | from 5 minutes to hours |
| Peak handling | ∞ parallel calls | limited by staff |
| Call cost | cost calculated after audit; many times lower | high (FTE, taxes) |
| Logging accuracy | 100% (audio recording + JSON) | errors possible |
| Scenario flexibility | LLM adaptation to context | training required |
Implementation stages and timelines
| Stage | Content | Duration |
|---|---|---|
| Analysis | Process audit, script collection, trigger definition | 1–2 days |
| Design | Dialogue diagrams, integration schemes, model selection | 2–3 days |
| Implementation | MVP with basic scenario + retry logic | 5–7 days |
| Integration | API setup with CRM, 1C, payment gateways | 2–4 days |
| Testing | 100% scenario coverage, load testing (100+ parallel calls) | 2–3 days |
| Documentation | Call flow description, operator instructions, API specification | 1–2 days |
| Training | Operator training on handling escalations | 1 day |
| Support | 2 weeks of monitoring and scenario adjustment | 14 days |
What is included in the work
- Dialogue diagrams and integration schemes in Confluence
- API documentation and operator instructions
- Access to the metrics dashboard (conversion, latency, error rate)
- Team training and 2 weeks of post-release support
- Ability to customize scenarios for new business processes
How we do it: stack and architecture
A typical solution is built on microservices:
- FastAPI — API for managing orders and the call queue
- PostgreSQL + pgvector — dialogue storage and semantic search by intent
- WebSocket — real-time status synchronization
- Triton Inference Server / vLLM — LLM inference with latency p99 < 500 ms
- ChromaDB — vector storage for few-shot examples
The call is initiated via SIP trunk or WebRTC, audio streaming to ASR (Whisper or proprietary models), the response is generated by LLM, and speech is synthesized by TTS (Silero, Coqui, or ElevenLabs).
Guarantee and team experience
Our specialists have 5+ years in voice assistants, 30+ launched projects in e-commerce in Russia. We have certificates for integration with 1C and Bitrix24.
According to internal statistics, average client savings are 30–50% of operator department costs — for example, if you previously had monthly operator department costs, after bot implementation the costs are calculated after audit and are significantly lower.
We guarantee correct processing of 100% of the provided dialogue branches and a reduction in the non-purchase rate to 5–8% within the first month.
Get a consultation — test the quality of dialogues on your own scenarios. Order a demo call: contact us to discuss integration and receive a commercial proposal.