Note: when a client writes in Telegram and then clarifies details in WhatsApp an hour later, the operator has to ask again — the history is lost. As a result, companies lose up to 30% of inquiries due to the lack of a unified window. Our AI contact center solves this problem: we integrate Telegram, WhatsApp, Viber and VK into one interface. One AI agent handles all channels, escalating complex requests to an operator with full context. We have completed over 100 such integrations and guarantee 99.9% uptime.
Telegram
Telegram Bot API is the fastest integration method. Supported: text, voice messages (transcription via Whisper), documents, photos. For business accounts, we use the Telegram Business API. Message delivery speed is under 1 second, making Telegram the leader in responsiveness. Load tests show p99 latency of 300 ms at 500 requests per second.
WhatsApp Business API (Cloud API Meta or BSP) gives access to template messages for proactive notifications. Supports rich media, location, documents. Properly configured, it's GDPR compliant. We implemented integration for a retail chain: handling 50,000 inquiries per month with p99 latency = 800 ms. Delivery speed ~500 ms — 2x faster than using separate bots.
Viber
Viber API provides a basic set: text, images, buttons. Relevant for Eastern European markets. Chatbot + Business Messages. Delivery speed is lower than Telegram and WhatsApp (1-2 s) but sufficient for support. During integration, we optimize long polling to reduce timeouts.
VK Integration
VK API for community messages and VK Bots. We support callback requests, keyboard, longpoll. Critical for the Russian market. Delivery speed 1-3 s — adequate for asynchronous dialogues.
Configuring WhatsApp Integration: Step-by-Step Guide
- Register a business account with Meta and verify the phone number.
- Connect Cloud API via webhook.
- Configure message templates (template messages) for notifications.
- Develop a handler in Python/FastAPI.
Example minimal handler:
@app.post("/webhook/whatsapp") async def handle_whatsapp(request: Request): data = await request.json() message = data["entry"][0]["changes"][0]["value"]["messages"][0] text = message.get("text", {}).get("body", "") response = ai_agent.process(text) await send_whatsapp(message["from"], response) return {"status": "ok"} Why Omnichannel Architecture is Critical?
Without it, each channel lives its own life. The client writes in Telegram, the operator replies, but an hour later the client clarifies in WhatsApp — and the operator has to ask again. Our middleware normalizes incoming messages, enriches them with history, and passes them to the AI agent. The agent sees the full context: the last 10 messages from any channel, tags, status. When escalation occurs, the entire dialogue branch is passed to the operator.
"After implementing the integration, client service became 40% faster and operator costs decreased by 30%" — retail client testimonial.
Compare this to a solution with separate bots: you pay triple support, lose context, and annoy the client. Our integration processes 1000 messages/min with p99 latency < 1 s — 2x faster than typical solutions.
| Channel | API | Speed | Rich media | Voice |
|---|---|---|---|---|
| Telegram | Bot API | < 1 s | Yes | Whisper |
| Cloud API | ~500 ms | Yes | No | |
| Viber | Viber API | 1-2 s | Images | No |
| VK | VK API | 1-3 s | Documents | No |
Server Requirements
Minimum: 2 CPU, 4 GB RAM, 50 GB SSD. Recommended: 4 CPU, 8 GB RAM, 100 GB SSD. All major clouds (AWS, GCP, Azure) and bare-metal are supported.What is Included in the Deliverable?
- Audit of current infrastructure and channels
- Design of omnichannel architecture (middleware, queues, database)
- Development and configuration of integration with each channel
- Connection of AI agent (OpenAI GPT-4, Claude 3.5, or LLaMA 3)
- Load testing: target 1000+ messages/min with p99 < 1 s
- Training operators on the unified interface
- API documentation and deployment schematics
- Post-launch support (2 weeks of monitoring)
Response Time Comparison Before and After Integration
| Metric | Before integration (separate bots) | After integration (our solution) |
|---|---|---|
| p99 latency | >2 s | <1 s |
| Context | None | Full (last 10 messages) |
| Escalation | Manual, with history loss | Automatic, with context |
| Operator training | Separate per channel | Unified |
Timelines and Pricing
Timelines: 2–3 weeks for the first channel, +1–2 weeks for each additional channel. Full project (4 channels) — 3–5 weeks. Pricing is calculated individually based on integration complexity, number of channels, and need for AI agent customization. ROI through operator cost savings — 3–6 months.
We have completed over 100 AI contact center integrations with messengers. Our engineers are certified on AWS and GCP. We guarantee no data loss during channel switching. Evaluate your project — contact us for a consultation. Get a detailed plan and timeline tailored to your task.