Real-time concurrent voice infrastructure processing 500+ simultaneous calls with zero-latency voice-to-data ingestion engines.
Production-grade voice AI infrastructure designed for real-time, concurrent call processing at scale. Built to handle 500+ simultaneous voice streams with zero-latency ingestion, real-time transcription, and intelligent routing.
This system powers the voice AI capabilities at Reallytics.ai, processing live audio streams into structured business data for downstream analytics and AI-driven decision-making.
┌─────────────────────────────────────────────┐
│ Load Balancer (ALB) │
└────────────────────┬────────────────────────┘
│
┌────────────────────▼────────────────────────┐
│ WebSocket Gateway (FastAPI) │
│ - Connection management │
│ - Audio stream ingestion │
│ - Session tracking │
└────────────────────┬────────────────────────┘
│
┌────────────────────▼────────────────────────┐
│ Apache Kafka Cluster │
│ - Audio chunk streaming │
│ - Event-driven processing │
│ - Partition-based scaling │
└──────┬─────────────┬──────────────┬─────────┘
│ │ │
┌────────────▼──┐ ┌───────▼───────┐ ┌──▼────────────┐
│ STT Worker │ │ Sentiment │ │ Analytics │
│ (gRPC/C++) │ │ Analyzer │ │ Engine │
│ - Whisper │ │ - Real-time │ │ - Metrics │
│ - Custom ASR │ │ - Emotion │ │ - Insights │
└───────┬───────┘ └───────┬───────┘ └──┬────────────┘
│ │ │
┌───────▼──────────────────▼──────────────▼─────────┐
│ Results Aggregator │
│ - Structured output │
│ - Real-time dashboard feed │
└──────────────────────┬────────────────────────────┘
│
┌──────────────────────▼────────────────────────────┐
│ Data Store (PostgreSQL + Redis) │
└───────────────────────────────────────────────────┘
- Zero-Latency Ingestion: WebSocket-based audio stream ingestion with sub-50ms processing latency
- Massive Concurrency: Handles 500+ simultaneous voice calls through Kafka partition-based scaling
- High-Performance STT: gRPC microservices with C++ modules (CUDA, Eigen) for speech-to-text inference, reducing latency by 25%
- Real-Time Sentiment Analysis: Live emotion and sentiment detection on voice streams
- Streaming Architecture: Apache Kafka for event-driven, fault-tolerant audio chunk processing
- Cloud-Native Deployment: AWS ECS/ECR with Docker containerization, auto-scaling, and health monitoring
- Sales Insights Pipeline: Real-time extraction of business signals from voice conversations
| Category | Technologies |
|---|---|
| Core | Python, C++ (CUDA, Eigen), FastAPI |
| Streaming | Apache Kafka, WebSockets, gRPC |
| ML/AI | Whisper, Custom ASR models, Sentiment models |
| Cloud | AWS (ECS, ECR, Lambda, S3, RDS), Docker |
| Data | PostgreSQL, Redis, Real-time streaming |
| Monitoring | Grafana, CloudWatch, Custom dashboards |
| Metric | Value |
|---|---|
| Concurrent calls supported | 500+ |
| Audio ingestion latency | < 50ms |
| STT inference latency | < 200ms (with C++/CUDA) |
| Sentiment analysis latency | < 100ms |
| System uptime | 99.9% |
| Latency reduction (vs pure Python) | 25% |
| Additional concurrent users supported | +15% |
Source Code: The production source code for this project is maintained in a private repository due to proprietary and client confidentiality requirements. This repository documents the architecture, design decisions, and technical approach. For code-level discussions or collaboration inquiries, feel free to reach out.
Rehan Malik - CTO @ Reallytics.ai