Enterprise voice pipelines require careful architectural consideration to integrate with existing IT infrastructure while maintaining security, scalability, and reliability. Our approach at Lexia centers on modular pipeline design that enables component-level customization and replacement without system-wide disruption.
The core pipeline architecture follows a microservices pattern, with distinct services for audio ingestion, preprocessing, model inference, post-processing, and integration. Each service communicates via well-defined APIs (typically gRPC for inter-service communication, REST for external interfaces), enabling independent scaling and deployment. This architecture has allowed us to achieve 99.9% uptime even during individual service updates or failures.
Audio preprocessing is more critical than many teams realize. Raw audio from enterprise sources varies dramatically in quality—call center recordings compressed with GSM codecs, meeting audio from VoIP systems with packet loss, mobile recordings with significant background noise. Our preprocessing pipeline includes noise reduction algorithms (primarily spectral subtraction and Wiener filtering), automatic gain control, and voice activity detection. These steps, while adding 20-30ms latency, improve downstream WER by 2-4 percentage points on noisy audio.
Integration with existing enterprise systems requires careful consideration of data formats, authentication mechanisms, and data residency requirements. We've developed adapters for major CRM platforms (Salesforce, HubSpot, Microsoft Dynamics), ticketing systems (Jira, ServiceNow), and communication platforms (Slack, Teams). Each adapter handles platform-specific authentication (OAuth 2.0, SAML, API keys), data transformation, and error handling. The adapter pattern allows us to add new integrations without modifying core pipeline code.
Real-time processing pipelines differ significantly from batch processing architectures. For live transcription during calls or meetings, we use WebSocket connections for bidirectional communication. The client sends audio chunks (typically 1-2 second segments) as they're captured, and our pipeline returns partial transcriptions in near-real-time. We implement incremental decoding that updates transcriptions as more audio is processed, providing users with immediate feedback while maintaining accuracy.
Batch processing for archived recordings requires different optimizations. We process large batches of audio files using distributed computing frameworks, prioritizing throughput over latency. Our batch pipeline can process thousands of hours of audio across GPU clusters, with automatic load balancing and failure recovery. Checkpointing mechanisms ensure we don't lose progress if processing is interrupted.
Data privacy and security are paramount in enterprise deployments. We implement end-to-end encryption for audio in transit (TLS 1.3) and at rest (AES-256). Access control uses role-based access control (RBAC) with fine-grained permissions. Audit logging captures all access and processing activities for compliance. For particularly sensitive deployments, we support air-gapped installations where all processing happens on-premises without any external network connections.
The post-processing layer transforms raw transcriptions into structured data suitable for enterprise systems. Named entity recognition (NER) extracts people, organizations, dates, and other structured information. Sentiment analysis provides emotion labels for customer interactions. Intent classification categorizes conversations for routing or analytics. This structured data is then formatted according to destination system requirements—for example, creating Salesforce tasks, Jira tickets, or database records.