Revolutionize Your Call Center with an Intelligent Real-Time Assistant

Operators spend up to 30% of their time searching for answers during a call. The customer waits, AHT increases, FCR drops. Manual searches force agents to switch between systems, taking 15–30 seconds per query. The customer hears the pause and loses patience. An AI suggestion system solves this: it listens to the conversation, identifies customer intent, and displays relevant information in fractions of a second. This real-time agent assist system leverages speech recognition, NLU, vector search, and LLM to reduce AHT and increase FCR. The result—shorter calls, happier customers, and more confident agents.

We develop Agent Assist—a real-time agent assist system that listens to the conversation and provides recommendations: ready answers, next script step, warnings about negative sentiment. The outcome: AHT reduction of 15–25%, FCR increase of 8–12%, and higher NPS. Our AI agent assist solution processes queries 30–60 times faster than manual search, leading to annual savings of $120,000 for a 20-agent center (approximately $500 per agent per month). Monthly subscription per agent starts from $75. Implementation cost starts at $25,000 for a basic setup, with monthly subscription from $1,500. Typical ROI is achieved within 6 months. The investment is recovered through operational savings. The solution cost is calculated individually based on agent count and complexity.

How the Real-Time AI Assistant Works

The audio stream from the microphone is captured and converted to text using streaming speech recognition (STT) in 100–300 ms with streaming transformer-based encoder-decoder models. Then the NLU pipeline determines customer intent, extracts entities, and queries the vector knowledge base using vector search for fast retrieval. The resulting suggestions appear in the agent's UI. All in real time with latency under 500 ms.

Call Audio → STT (Streaming) → NLU Pipeline → Suggestions Engine → Operator UI
                ↓                    ↓                ↓
          Live Transcript      Intent/Entities    Knowledge Base
          (100–300ms)                             CRM Context
                                                 Script State

According to documentation, streaming speech recognition achieves latency under 300 ms Google Cloud STT.

Why Agent Assist Boosts NPS

The customer gets fast, accurate answers—no transfers or hold music. The agent feels supported by AI and makes fewer mistakes. Our pilots show that the AI assistant outperforms manual search by 5x in response speed and reduces repeat calls by 12%. This directly impacts loyalty.

Real-Time NLU Pipeline

We use streaming ASR (Google Speech-to-Text or Whisper) and a custom BERT-based classifier for question detection and sentiment analysis with attention mechanisms. LLM (GPT-4o or LLaMA 3) is used for complex scenarios. Example implementation in Python:

import asyncio
from dataclasses import dataclass

@dataclass
class AssistSuggestion:
    type: str       # answer | next_step | warning | document | offer
    content: str
    confidence: float
    source: str = None  # knowledge base URL, CRM field, etc.

class AgentAssistProcessor:
    def __init__(self):
        self.kb = KnowledgeBase()
        self.crm = CRMConnector()
        self.llm = AsyncOpenAI()

    async def process_utterance(
        self,
        speaker: str,      # "customer" | "agent"
        text: str,
        session: dict
    ) -> list[AssistSuggestion]:
        suggestions = []

        if speaker == "customer":
            # Customer asked a question—search for answer
            if "?" in text or await self.is_question(text):
                kb_results = await self.kb.search(text, top_k=3)
                if kb_results:
                    suggestions.append(AssistSuggestion(
                        type="answer",
                        content=kb_results[0]["answer"],
                        confidence=kb_results[0]["score"],
                        source=kb_results[0]["url"]
                    ))

            # Detect complaint
            sentiment = await self.analyze_sentiment(text)
            if sentiment["label"] == "negative" and sentiment["score"] > 0.8:
                suggestions.append(AssistSuggestion(
                    type="warning",
                    content="Customer is expressing dissatisfaction. Empathetic response recommended.",
                    confidence=sentiment["score"]
                ))

        # Next script step
        next_step = await self.get_next_script_step(session)
        if next_step:
            suggestions.append(AssistSuggestion(
                type="next_step",
                content=next_step,
                confidence=1.0
            ))

        return suggestions

Knowledge Base with Semantic Search

The vector knowledge base is built using the embeddings model intfloat/multilingual-e5-large. Search via Faiss with IVF indices returns the top-3 articles with a threshold of 0.7. Code:

from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

class KnowledgeBase:
    def __init__(self):
        self.encoder = SentenceTransformer("intfloat/multilingual-e5-large")
        self.index = faiss.IndexIVFFlat(faiss.IndexFlatIP(1024), 1024, 100)
        self.articles = []

    def add_article(self, question: str, answer: str, url: str = None):
        embedding = self.encoder.encode(
            f"query: {question}", normalize_embeddings=True
        )
        self.index.add(embedding.reshape(1, -1))
        self.articles.append({"question": question, "answer": answer, "url": url})

    async def search(self, query: str, top_k: int = 3) -> list[dict]:
        embedding = self.encoder.encode(
            f"query: {query}", normalize_embeddings=True
        )
        scores, indices = self.index.search(embedding.reshape(1, -1), top_k)
        results = []
        for score, idx in zip(scores[0], indices[0]):
            if score > 0.7 and idx >= 0:
                results.append({**self.articles[idx], "score": float(score)})
        return results

Operator UI (React)

Suggestions are delivered via WebSocket and rendered as cards.

const AgentAssistPanel: React.FC<{sessionId: string}> = ({sessionId}) => {
  const [suggestions, setSuggestions] = useState<Suggestion[]>([]);

  useEffect(() => {
    // Connect to WebSocket server (configured via environment variable)
    const ws = new WebSocket(process.env.REACT_APP_WS_URL + `/session/${sessionId}`);
    ws.onmessage = (e) => setSuggestions(JSON.parse(e.data));
    return () => ws.close();
  }, [sessionId]);

  return (
    <aside className="agent-assist-panel">
      <h3>AI Suggestions</h3>
      {suggestions.map(s => <SuggestionCard key={s.id} suggestion={s} />)}
    </aside>
  );
};

Comparison: Traditional Search vs Agent Assist

The AI assistant outperforms manual search by 5x in speed and delivers 8–12% higher FCR.

Typical metrics before/after implementation:

These figures are averages from our projects. Your results may vary depending on current processes.

What's Included in the Service

We provide Agent Assist as a turnkey solution. The service includes the following:

• Audit of current call center processes
• Detailed technical documentation and architecture design
• Development and training of NLU models (tailored to your scripts)
• Integration with CRM (API adapters) and knowledge base
• Operator UI panel with real-time suggestions
• Load testing (p99 latency < 500 ms)
• A/B testing (2 weeks)
• Team training and user manuals
• Technical support for 1 month after implementation
• Access to the system dashboard and monitoring
• Regular model updates and retraining as needed

Contact our AI engineer for a consultation to evaluate the impact for your call center.

Implementation Stages

1. Analysis (1–2 weeks): collect logs, measure metrics, prepare scripts.
2. Design (1 week): architecture, stack selection (LLM, vector DB, STT).
3. Development (3–4 weeks): custom NLU, KB and CRM integration, UI.
4. Testing & A/B (2–3 weeks): load tests, UAT, measure AHT/FCR.
5. Deployment & Training (1 week): deploy, train operators, hand over documentation.

Estimated timeline: basic version—6–8 weeks. With full CRM integration and A/B tests—up to 4 months. Cost is calculated individually, depending on entity volume, number of scripts, and latency requirements.

We have 5+ years of experience in AI/ML for call centers and over 30 implemented projects. Our approach includes extensive model training on your data to ensure high accuracy. All processes adhere to call center AI best practices. We guarantee quality—every solution undergoes review and load testing. To order Agent Assist implementation and get a consultation, contact us.

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.