As AI voice analytics continues to reshape how businesses understand and act on spoken interactions, the demand for real-time, high-quality voice data integration is rapidly increasing. A common challenge organizations face is how to get access to the real-time voice streams for AI analytics. One way to address this is to use the existing call recording infrastructure based on the SIPRec protocol and convert SIPRec-based voice streams into WebSocket-compatible formats for downstream analytics applications.
While SIPRec (Session Recording Protocol) is an established standard in the telco world, WebSocket has become the go-to transport for modern, event-driven systems—especially for web-based dashboards, transcription services, and AI-powered analytics engines.
But connecting these two worlds is anything but simple.
SIPRec was designed for compliance recording and initiates a SIP session with multipart MIME bodies that contain SDP (Session Description Protocol) for media setup and metadata for contextual details. The media itself is streamed over RTP (Real-time Transport Protocol), often in codecs like G.711 or G.722.
WebSocket is widely used in the web developer community to stream data into for example analytics applications.
Bridging between the two worlds requires deep protocol translation and thoughtful handling of session state, timing, voice encoding and reliability.
Once the SIPRec session is established, voice media streams start flowing via RTP. These RTP packets must be received, ordered, and decoded in real time. This involves:
RTP handling is not trivial, especially when multiple calls are processed simultaneously. Developers must implement jitter buffers, sequence number tracking, and codec decoding—typically with the help of libraries like FFmpeg, GStreamer, or custom-built media engines.
The AI analytics service expects audio in specific formats, usually linear PCM, and predictable chunk sizes. It is common to use WebSocket as a transport protocol to receive voice media.
Unlike RTP, WebSocket does not have built-in timing or ordering mechanisms, so developers must implement custom signaling and synchronization to ensure the downstream AI analytics service can make sense of the stream.
As with any real-time system, scaling SIPRec-to-WebSocket conversion across multiple concurrent sessions requires robust infrastructure. This means:
You also need to ensure fault tolerance in case an RTP stream stalls or a WebSocket connection is interrupted. Stateless design patterns, retry mechanisms, and circuit breakers become critical here.
The ability to access real-time voice streams is key to enable AI voice analytics, and the underlying telecom complexity involved in this should not be underestimated.
Transcoding SIPRec voice streams into WebSocket-compatible formats is one way to enable AI Voice analytics in modern applications. However, it introduces significant engineering challenges across protocol translation, media handling, synchronization, and scalability.
Organizations attempting this should consider building a modular middleware service that can:
iotcomms.io’s AI Connect Service is a cloud-native service that simplifies accessing real-time voice streams and metadata for AI-based voice analytics. It takes care of the telecom challenges related to bridging telephony systems with AI analytics services.
Delivered as a SaaS, the AI Connect Service offers businesses an operations-efficient alternative to integrating to the telephony system themselves and takes care of the telecom complexity mentioned earlier in this blogpost. Furthermore, with iotcomms.io running the service in the cloud, businesses don’t need to worry about upgrades, updates, or issues related to hardware or software scaling.
The AI Connect Service seamlessly integrates voice calls with analytics services over WebSocket, but also with analytics services from AWS and Google, such as Amazon Transcribe, Amazon Lex, Google Contact Center AI (CCAI) platform and Google Dialogflow. The integrations to AWS and Google’s services are however not focused on in this blog post.
As can be seen in the illustration below, the AI Connect Service captures the voice stream and metadata of the caller and callee from the telephony system (a PBX or Session Border Controller (SBC)) using the standardized SIPRec protocol interface, routes the call and performs media handling before delivering it to the analytics platform in the WebSocket format. It can be output as an interleaved stream over a single WebSocket or as two separate streams in two WebSocket connections.
