Support teams lose up to 30% of customers due to unrecognized negativity during calls. Half of those customers churn to competitors without waiting for a resolution. Each month, companies lose substantial sums (example: a 50-operator call center). The average loss from lost customers can reach about $9k–13k in savings per year. We build Live Sentiment Analysis — an AI system that evaluates the emotional state of the customer in real-time from voice and transcript, instantly signaling the operator or supervisor. The solution is turnkey: from data collection to deployment in your contact center. An average project handling 1000 concurrent calls processes up to 50 tokens per second with p99 latency of 450 ms.
Sentiment analysis live is not just a buzzword—it's a tool with proven effectiveness. In A/B tests on real streams, we recorded a 20% reduction in churn within the first month. Request a demo to evaluate effectiveness on your calls.
Why real-time analysis is critical for a call center?
Standard post-call surveys and recording reviews are outdated. The customer hangs up with negativity, and you find out a day later. Our AI analyzes every phrase and voice accent, displaying an indicator on screen with <500ms latency. This allows the operator to adjust the conversation before the customer leaves.
| Approach | Latency | Accuracy | Applicability |
|---|---|---|---|
| Post-processing | >24 hours | ~90% | Archival analysis |
| Streaming text | 300-500 ms | ~80% | Real-time |
| Fusion (text+acoustic) | <600 ms | >85% | Critical moments |
The fusion model is 1.5x more accurate than text-only, and acoustic analysis is 3x faster than text-only.
How the fusion model improves accuracy?
Text analysis of transcript (latency 300–500 ms)
We use RuBERT from Hugging Face, fine-tuned on Russian-language reviews. Processing code:
from transformers import pipeline import asyncio sentiment_analyzer = pipeline( "sentiment-analysis", model="blanchefort/rubert-base-cased-sentiment-rurewiews", tokenizer="blanchefort/rubert-base-cased-sentiment-rurewiews" ) async def analyze_utterance_sentiment(text: str) -> dict: result = sentiment_analyzer(text[:512])[0] # limit length return { "label": result["label"], # POSITIVE | NEGATIVE | NEUTRAL "score": result["score"], "text": text } Acoustic analysis of voice (without transcription, latency <100 ms)
We extract prosodic features directly from the audio stream—pitch, energy, speech rate. This is especially valuable when the text is neutral but the voice is "boiling".
import librosa import numpy as np def extract_acoustic_features(audio_chunk: bytes, sr: int = 16000) -> dict: """Extract prosodic features for emotion classification""" audio = np.frombuffer(audio_chunk, dtype=np.int16).astype(np.float32) / 32768.0 # Fundamental frequency (F0) — marker of emotional state f0, _ = librosa.pyin(audio, fmin=80, fmax=400, sr=sr) f0_mean = np.nanmean(f0) f0_std = np.nanstd(f0) # Speech rate tempo, _ = librosa.beat.beat_track(y=audio, sr=sr) # Energy rms = librosa.feature.rms(y=audio)[0] energy_mean = np.mean(rms) return { "f0_mean": float(f0_mean) if not np.isnan(f0_mean) else 0, "f0_std": float(f0_std) if not np.isnan(f0_std) else 0, "energy": float(energy_mean), "tempo": float(tempo) } Fusion: text + acoustic — 1.5x more accurate
Combining two channels creates synergy: when the text is neutral but the voice is agitated, the model classifies as "anxiety" or "irritation". Below is an example of the fusion layer code:
async def combined_sentiment(text: str, audio: bytes) -> dict: text_sentiment, acoustic_features = await asyncio.gather( analyze_utterance_sentiment(text), asyncio.get_event_loop().run_in_executor( None, extract_acoustic_features, audio ) ) # High energy + negative text = anger # Low energy + negative text = frustration emotion = classify_emotion(text_sentiment, acoustic_features) return { "sentiment": text_sentiment["label"], "emotion": emotion, "confidence": text_sentiment["score"], "acoustic_signals": acoustic_features } | Emotion | Text signal | Acoustic signal |
|---|---|---|
| Anger | Negative words | High F0, energy |
| Frustration | Negative words | Low F0, low energy |
| Sarcasm | Neutral words | Contradictory features |
WebSocket for real-time UI
@app.websocket("/sentiment-stream/{call_id}") async def sentiment_stream(websocket: WebSocket, call_id: str): await websocket.accept() async for event in get_call_events(call_id): if event["type"] == "customer_utterance": sentiment = await analyze_utterance_sentiment(event["text"]) await websocket.send_json({ "timestamp": event["timestamp"], "text": event["text"], **sentiment }) How CRM integration improves efficiency?
The system transmits every sentiment metric to your CRM via REST API. The supervisor receives push notifications about "red" calls and can join the conversation. We have already integrated with popular platforms: Bitrix24, amoCRM, Zendesk. All emotion history is stored in the database, enabling dashboards and identification of problem topics.
MLOps: how we maintain the model in production
To prevent data drift, we implement monitoring of key metrics (accuracy, class distribution). Every two weeks, validation is run on fresh labeled samples. If accuracy drops below 80%, fine-tuning on the latest 10,000 dialogs is automatically initiated. For model versioning, we use MLflow; inference runs on Triton Inference Server.
Process of work
- Analytics: audit of current infrastructure, collection of historical calls (minimum 1000 dialogs).
- Design: choice of architecture—edge or cloud inference, vectorization of chunks for RAG.
- Implementation: training/fine-tuning models, writing fusion layer, WebSocket endpoints.
- Testing: A/B experiment on 10% of calls, comparison with human audit.
- Deployment: containerization, scaling to peak load of your call center.
Common mistakes during implementation
- Too short phrases: the model requires at least 10 tokens for text analysis.
- Channel noise: poor quality recording reduces acoustic accuracy by 15-20%.
- Lack of labeled data: fine-tuning without a reference sample yields only 5% improvement.
What is included in the work
- Architecture and API documentation
- Source code of models and pipelines
- Integration with your CRM or telephony
- Team training (2-hour webinar + checklist)
- 3 months of post-launch support
Timelines and cost
- Basic text-only version: from 2 to 3 weeks.
- Full cycle (text + acoustic + training): from 4 to 6 weeks. Cost is calculated individually after evaluating data volume and infrastructure. Contact us for a consultation — we will assess your project within 1-2 days. You can also request demo access to the system on your calls.
5+ years in the AI solutions market, 50+ projects in NLP and Computer Vision. We guarantee quality: each stage is validated on real data. Get a solution that is not just "like/dislike" but a full-fledged emotion detector with an action focus.