We develop AI cold calling bots that automate lead qualification and CRM integration, reducing cost per lead by 5-10 times. Cold calling is one of the most expensive and least effective funnels — the average conversion rate rarely exceeds 2%, and the cost per lead keeps rising. Operators burn out, scripts are broken, and conversion drops by the end of the day. A voice AI bot solves this fundamentally: 1000+ parallel calls, consistent script quality, objection handling without fatigue. Compared to human operators, the bot achieves a 2x higher qualification rate and a 10x reduction in cost per qualified lead (from $4–6 to $1–1). We have been implementing such solutions for over 7 years with 100+ projects — we have accumulated experience that allows us to launch a campaign in 3-4 weeks. Salary savings on operators can reach 70% — for a department of 10 people, this is about $27k–39k per year.
Why is a voice bot more effective than an operator?
Compare: an operator makes 50–80 calls per day, of which 10–15 are productive. An AI bot makes 1000+ calls per day with a contact rate of 30–50% and a qualification rate of 15–25%. The cost per qualified lead is reduced by 5–10 times, and operator salary savings can reach 70%. The bot is not afraid of rejections, does not lose enthusiasm, and strictly follows the script — no improvisations that could harm the brand. Our voice AI bot combines TTS/STT with objection handling NLU to scale cold calls and automate lead qualification.
What technical challenges do we solve?
The first problem is integration with telephone infrastructure. We connect the bot via SIP trunk or cloud PBX (Asterisk, Miko, Zadarma). The second is natural language recognition in noisy channels. We use Whisper + fine-tuned NLU for objection detection. The third is passing context to CRM. We implement webhooks and API calls at each status transition. The cost per minute of bot conversation is 3–5 times lower than an operator. Our solution leverages a microservices architecture with gRPC for low-latency streaming, and employs a rules-based fallback for scripted portions.
Case study: implementation for a fitness club chain
From our practice: a fitness club chain (50+ clubs) — one of the projects. It was required to call 200,000 contacts in a week, qualify interest in an annual membership, and book a trial visit. We deployed a cluster of 20 parallel bot sessions based on LangChain using OpenAI GPT-4o for generating responses to non-standard objections. Integration with Bitrix24 via REST API — each lead was created with filled fields: interest level, preferred time, source. Result: 180,000 calls reached, 45,000 qualified leads, 12,000 trial visit bookings. Contact rate — 34%, qualification rate — 22%.
Script and NLU configuration
The cold call script is built on a modular principle: hook, qualification questions, objection handling, offer. For each objection, a separate handler is written; for unknown objections, LLM is connected. The script is easily adaptable to your industry.
OBJECTION_HANDLERS = { "not_interested": { "detect": ["not interested", "don't need", "not relevant"], "response": "I understand. What would be interesting in the context of [problem]?" }, "busy": { "detect": ["busy", "not a good time", "call back"], "response": "Of course! When is better to call back — this evening or tomorrow morning?" }, "we_have_solution": { "detect": ["already have", "work with", "another provider"], "response": "Great! Many clients use us alongside [competitor] for [unique value]." }, "send_info": { "detect": ["send", "email", "mail"], "response": "Gladly! What email should I send the materials to?" } } async def handle_objection(text: str) -> tuple[str, str]: text_lower = text.lower() for objection_type, handler in OBJECTION_HANDLERS.items(): if any(phrase in text_lower for phrase in handler["detect"]): return objection_type, handler["response"] return "unknown", await generate_response_with_llm(text) Comparison of speech recognition solutions
| Solution | Quality in noise | Latency | Cost |
|---|---|---|---|
| Whisper (OpenAI) | High | Medium | Free (self-host) |
| Google Cloud STT | High | Low | $0.006/min |
| Silero | Medium | Low | Free (self-host) |
| ElevenLabs (TTS) | — | Low | $0.20/thousand characters |
What's Included (Deliverables)
- Development of a cold call script with hook, qualification questions, and objection handling
- NLU configuration for detecting 10+ rejection types with >90% accuracy (trained on 5,000+ transcripts)
- Integration with your CRM (Bitrix24, amoCRM, HubSpot, 1C) — two-way synchronization via REST API
- Opt-out system with automatic blacklist addition (GDPR/152-FZ compliant)
- Call recording and listening in CRM
- Dashboard access with real-time metrics
- Team training on dashboards and logs
- 2 months post-launch support with documentation
Implementation stages
- Analytics: audit of scripts, collection of typical objections, funnel setup.
- Design: NLU architecture, TTS/STT selection, integration scheme design.
- Development: script coding, objection model training, logic for transfer to operator.
- Testing: A/B test of bot vs operators, metric evaluation, threshold adjustments.
- Deployment: deployment on client infrastructure (on-prem or cloud), first campaign launch.
Metrics and company experience
With 7+ years in AI voice solutions, we have completed over 100 projects for mid-market and enterprise clients. Our system is ISO 27001 certified for information security. Guaranteed 99.9% uptime for production deployments.
| Metric | Target value | Typical result at start |
|---|---|---|
| Contact Rate | 30-50% | 20-30% (after list cleaning) |
| Qualification Rate | 15-25% | 10-15% |
| Transfer Rate | 10-20% | 5-10% |
| Cost per Qualified Lead | Reduction by 5-10x (from $4–6 to $1–1) | — |
Timelines are calculated individually: MVP with basic script — 3-4 weeks, full solution with objection handling and CRM integration — up to 2 months. Contact us for an audit of your business process. We will evaluate the project in 1-2 days and offer an optimal solution. Request a launch of an AI bot for cold calls and get a consultation.