Air AI Platform Implementation for Autonomous Phone Sales

Autonomous Telephone Sales with Air AI Air AI is a specialized platform for autonomous telephone sales, positioned as a "human-sounding agent." The core technology is multimodal speech generation with emotional variations, pauses, and verbal fillers ("um," "well," "so") that make the conversation less robotic. ### Key Technology Features Long-duration conversations — Air AI is optimized for conversations lasting 15-40 minutes: it preserves context, doesn't "forget" details from the beginning of the conversation, and manages the narrative throughout the entire conversation. Humanization layer is a set of techniques for imitating human speech: - Uneven speech rate (speeding up/slowing down according to meaning) - Phatic reactions ("of course", "I understand", "excellent") - Imitation of thought ("let me check...") - Variable wording of the same question Infinite memory - between calls from one client, the agent remembers all previous conversations without explicitly conveying the history.

Integration and automation

import requests
import json
from datetime import datetime

class AirAIClient:
    """Работа с Air AI через REST API"""

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.air.ai"
        self.headers = {
            "x-api-key": api_key,
            "Content-Type": "application/json"
        }

    def create_agent(self, name: str,
                      persona: str,
                      mission: str,
                      voice_settings: dict = None) -> dict:
        """
        Создание агента с заданной персоной и целью.
        persona: описание характера агента (дружелюбный, профессиональный и т.д.)
        mission: описание задачи (продажа, квалификация, опрос)
        """
        payload = {
            "name": name,
            "persona": persona,
            "mission": mission,
            "voice": voice_settings or {
                "gender": "female",
                "accent": "ru-RU",
                "speed": 1.0,
                "emotion_variation": 0.7  # 0-1, насколько эмоционален
            }
        }

        return requests.post(
            f"{self.base_url}/agents",
            json=payload,
            headers=self.headers
        ).json()

    def initiate_sales_call(self, agent_id: str,
                             lead: dict,
                             call_objective: str) -> dict:
        """
        Звонок с целью продажи.
        lead: {'phone': '...', 'name': '...', 'company': '...', 'context': '...'}
        call_objective: конкретная цель звонка (записать демо, закрыть сделку)
        """
        payload = {
            "agent_id": agent_id,
            "phone_number": lead["phone"],
            "contact_info": {
                "name": lead.get("name", ""),
                "company": lead.get("company", ""),
                "background": lead.get("context", ""),
            },
            "objective": call_objective,
            "max_duration_minutes": 30,
        }

        return requests.post(
            f"{self.base_url}/calls",
            json=payload,
            headers=self.headers
        ).json()

    def get_call_outcomes(self, call_id: str) -> dict:
        """
        Результаты звонка: итог, следующий шаг, извлечённые данные.
        """
        response = requests.get(
            f"{self.base_url}/calls/{call_id}/outcomes",
            headers=self.headers
        )
        return response.json()

    def setup_follow_up_sequence(self, agent_id: str,
                                  contacts: list[dict],
                                  sequence_config: dict) -> dict:
        """
        Многошаговая последовательность обзвонов.
        sequence_config: {'max_attempts': 5, 'intervals_hours': [24, 48, 72, 168]}
        """
        payload = {
            "agent_id": agent_id,
            "contacts": contacts,
            "sequence": sequence_config,
            "stop_on_outcome": ["booked", "not_interested", "converted"]
        }

        return requests.post(
            f"{self.base_url}/sequences",
            json=payload,
            headers=self.headers
        ).json()
```### Practical Metrics of Air AI in Sales | Metric | Air AI | Junior SDR | |---------|---------|-----------| | Calls per hour | 8-12 | 8-15 | | Call duration | 3-25 min | 3-20 min | | Qualification (lead → MQL) | 15-25% | 20-35% | | Cost per qualified lead | $8-25 | $40-120 | | Availability | 24/7 | 8 hours/day | ### Limitations and Ethical Considerations Air AI positions agents as autonomous, which raises disclosure issues. In some jurisdictions (including some US states), it is **mandatory** to identify yourself as an AI system upon the first request. In Russia and the CIS, there is no explicit requirement yet, but best practice is to inform the client.
The platform is not suitable for: complex technical sales (B2B enterprise with long sales cycles), sales requiring a mandatory demo, or regulated industries (financial services, healthcare without disclaimers). The optimal scenario is initial qualification and appointment scheduling with a transfer to a live sales representative for final closing. The first campaign launch period is 2-3 weeks.