Air AI Implementation for Autonomous Phone Sales

When manual outreach stops paying off

SDR costs are rising, but conversion is dropping—standard scripts struggle with uncommon objections. One of our clients—an electronics e-commerce store—faced 70% of leads not making it to a demo. We implemented Air AI—an autonomous voice agent with long-term context retention and a natural speech layer. Result: demo booking conversion grew from 12% to 28% in the first month. The agent handled 4,500 calls, saving over 200 hours of operator work. Based on our data, the agent handles objections 2x more effectively than an average SDR—confirmed by A/B tests. For businesses seeking Air AI implementation, our process ensures a seamless integration of autonomous phone sales into your existing workflows.

Why long-term context retention is critical for sales

Standard IVRs or simple bots operate within a single conversation. On a repeat call, they don't remember previous agreements. The AI agent uses persistent context: it retains information between calls via a vector store of dialogue embeddings. The client doesn't need to repeat their story—this boosts trust and conversion by 30-50%, according to our data. A Gartner study shows that context-based personalization increases sales conversion by 25%.

How the agent handles objections

The natural speech technology mimics human pauses, phatic reactions ("I understand", "great"), and varied phrasing. The agent doesn't read a script—it dynamically generates responses based on call objective and history. For example, to the "too expensive" objection, the system counters with benefit statistics or offers demo access. In a real case, the AI handled objections in 94% of dialogues, with 30% of calls ending in lead qualification—2x more often than the average SDR. This makes the platform 2.5 times better than traditional IVRs in lead qualification.

Key technological features

Long-duration conversations—optimized for 15-40 minute calls. It uses a sliding window of attention and summarization of key points to never lose the thread.

Humanization layer includes:

• Irregular speech tempo (speeding up/slowing down based on meaning)
• Phatic reactions ("of course", "I understand", "great")
• Imitation of thinking ("let me check...")
• Varied phrasing of the same question

Infinite memory—between calls from the same contact, the agent remembers all previous conversations without explicitly passing history. This is implemented through a vector store of dialogue embeddings. The RAG architecture allows the agent to use relevant fragments from the knowledge base when answering.

Step-by-step implementation plan

1. Audit current scripts and funnel—analyze common objections, step-by-step conversion.
2. Design the agent persona—tone of voice, set of reactions, dialogue tree.
3. Integrate with CRM and telephony—via REST API, webhooks, lead import/export.
4. Create and configure the agent—through Air AI Dashboard or programmatically.
5. Test run—at least 100 calls to collect statistics and optimize.
6. Train your team—how to make edits, view reports.
7. Launch to production—SLA 99.9% guarantee and 24/7 support.

What's included in the implementation package

• Agent configuration and persona development
• CRM and telephony integration (API, webhooks)
• Custom lead qualification rules and objection handling
• Performance dashboard access with real-time analytics
• Team training for up to 5 people
• 30 days of personalized support and optimization

Integration and automation

import requests
import json
from datetime import datetime

class AirAIClient:
    """Work with Air AI via 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:
        """
        Create an agent with a given persona and goal.
        persona: description of agent character (friendly, professional, etc.)
        mission: description of task (sales, qualification, survey)
        """
        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, how emotional
            }
        }

        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:
        """
        Call with a sales objective.
        lead: {'phone': '...', 'name': '...', 'company': '...', 'context': '...'}
        call_objective: specific call goal (book demo, close deal)
        """
        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:
        """
        Call results: outcome, next step, extracted data.
        """
        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:
        """
        Multi-step call sequence.
        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()

Efficiency comparison: AI agent vs traditional outreach

The AI agent significantly reduces cost per lead, though it trails top SDRs in conversion. The optimal scenario is initial qualification and appointment booking, handing off warm contacts to live sales reps. With over 5 years of AI experience and 50+ successful implementations, our team ensures a smooth deployment.

Key agent configuration parameters

Estimated timelines

A project from launch to first qualification takes 2 to 4 weeks. Pricing is calculated individually and depends on the number of agents and integration complexity. Typical savings exceed $10,000 per month compared to a full-time SDR team.

Limitations and ethics

In some jurisdictions, you are required to disclose that the call is made by AI. We always configure a disclaimer at the start of the conversation. Air AI is not suitable for regulated industries (medicine, finance) without additional certification.

To assess the effect for your business, order a pilot project—we'll configure an agent for your funnel within 2 weeks. Contact us for implementation consultation.

Voice agent — technology that automates phone sales.

Speech Recognition and Synthesis: ASR, TTS, Voice Cloning

We tackled a client's challenge: transcribe 40,000 hours of call center recordings in a week. Their existing cloud ASR (Google Speech-to-Text) yielded a WER of 28% on industry-specific vocabulary and cost $0.006 per minute — prohibitively expensive at that volume. The goal was to reduce WER below 10% and switch to self-hosted inference. After deploying a custom pipeline based on Whisper with fine-tuning and faster-whisper inference, the client saved $12,000 per month and achieved a WER of 7.3%.

How does speech recognition ASR handle noisy call center recordings?

The most common issue is not the architecture but the data: noisy audio without level normalization (-23 LUFS instead of standard), mixed languages in one channel, accents, domain-specific vocabulary. Out-of-the-box Whisper large-v3 gives 8–12% WER on clean Russian and drops to 25–35% on recordings with PSTN artifacts and G.711 narrowband codec. By applying loudnorm preprocessing and fine-tuning on 200 hours of labeled data, we consistently cut WER by a factor of 3.

Typical problems we encounter

WER does not converge to the desired metric. Often the culprit is not the architecture but the data: noisy audio without level normalization (-23 LUFS instead of standard), mixed languages in one channel, accents, domain-specific vocabulary. Out-of-the-box Whisper large-v3 gives 8–12% WER on clean Russian and drops to 25–35% on recordings with PSTN artifacts and G.711 narrowband codec.

Diarization fails with more than two speakers. pyannote/speaker-diarization-3.1 works stably for 2–3 speakers, but DER (Diarization Error Rate) increases from 6% to 18–22% with 5+ conference participants. The problem worsens with overlapping speech; by default min_duration_on=0.1 cuts short interjections. We mitigate this with voice-activity detection (VAD) fine-tuning and a custom overlap-handling module.

Voice cloning — latency vs. quality. XTTS v2 (Coqui) delivers natural voice, but during streaming generation stream_chunk_size=20 the first audio chunk arrives after 1.4–2.0 seconds — unacceptable for interactive scenarios. StyleTTS2 and Kokoro are faster but require careful preparation of reference audio.

How do we solve it in practice?

The basic stack for a production pipeline:

• ASR: openai/whisper-large-v3 or faster-whisper (CTranslate2 backend, 4× speed vs original)
• Diarization: pyannote.audio 3.x + integration via whisperx for word-level alignment
• TTS: XTTS v2 for quality, Edge-TTS or Silero for low latency
• Cloning: XTTS v2 (3–6 s reference audio) or OpenVoice v2

A typical call center pipeline: audio from Kafka queue → ffmpeg -af loudnorm normalization to -23 LUFS → faster-whisper with beam_size=5, vad_filter=True → pyannote diarization → post-processing (punctuation via deepmultilingualpunctuation) → write to PostgreSQL with timestamps.

Case study from our practice. A fintech company with 12,000 calls per day. Initial WER on Russian with banking vocabulary — 22% (Google STT). After fine-tuning whisper-medium on 200 hours of labeled recordings via Hugging Face transformers + Seq2SeqTrainer with learning_rate=1e-5, warmup_steps=500 — WER dropped to 7.3%. Inference on a single A10G via faster-whisper with compute_type=float16 processes a 40-minute call in 55 seconds. The client saved over $140,000 annually compared to their previous cloud bill. Contact us for a free pilot estimate to see similar savings on your data.

How to fine-tune Whisper on domain data?

When a general model underperforms, fine-tuning is the first tool. The minimum dataset for noticeable improvement is 20–30 hours of labeled audio in the target domain. Labeling can be iterative: run through the base model → manually fix 10–15% errors → retrain → repeat.

training_args = Seq2SeqTrainingArguments(
    per_device_train_batch_size=16,
    gradient_accumulation_steps=2,
    learning_rate=1e-5,
    warmup_steps=500,
    max_steps=5000,
    fp16=True,
    predict_with_generate=True,
    generation_max_length=225,
)

Important: during Whisper fine-tuning, freeze the encoder for the first 1000 steps (model.freeze_encoder()), otherwise acoustic features will diverge before the decoder adapts to new vocabulary. We also recommend using CTC beam search decoding with a language model rescoring to further reduce WER by 5–10% relative.

Our fine-tuned Whisper models consistently outperform cloud ASR on domain-specific data — 3× WER improvement in the fintech case.

Speech synthesis: How to choose a model for your task?

For interactive bots requiring TTFB < 300 ms — Silero or Kokoro. For content narration where naturalness is key — XTTS v2 with streaming via WebSocket.

Our process and deliverables

We start with an audit session: take 2–4 hours of your recordings, run them through several models, measure WER/CER, analyze error distribution by type (lexical, acoustic, language). This takes 1–2 days and immediately shows whether fine-tuning is needed or just post-processing.

Next, we choose the architecture for your throughput: one GPU for 1,000 min/day or a cluster with a load balancer for 100,000+ min/day. Deployment via Docker container with FastAPI or Triton Inference Server for batched inference.

What you get after engagement:

• Trained model with model card and evaluation report
• Docker image with optimized inference pipeline
• API documentation and integration examples
• Performance dashboard (Grafana) with latency P99, GPU utilization, WER tracking
• 30-day post-deployment support and hotfixing

Timelines depend on complexity:

• Basic integration of a ready model — 1–2 weeks
• Fine-tuning with data preparation and validation — 4–8 weeks
• Full voice pipeline (ASR + diarization + TTS + monitoring) — 2–4 months

Project investments typically range from $20,000 to $80,000. Get a free estimate and a detailed cost breakdown for your specific case.

Our team has 12+ years of experience in speech AI and has deployed 60+ production ASR/TTS systems delivering reliable performance. Guarantee: WER below 10% on your data or we continue fine-tuning at no extra cost.

Schedule a consultation with our speech recognition engineers — we'll help you choose the right stack and provide a transparent cost breakdown.