Insight into Safina AI, Part 1: The Core Architecture for Real-Time Speech AI

Welcome to the series "Insight into Safina AI"! Here you get an exclusive look behind the scenes of the technology that powers our AI phone assistant. The series is aimed at technical professionals, system architects, and anyone who wants to understand how robust, enterprise-grade AI solutions for speech are created. In today’s business world, telephony is no longer just about connecting calls. It’s about creating intelligent, responsive, and automated experiences. An AI that takes calls, books appointments, and answers complex questions needs an architecture designed for speed, reliability, and deep integration. In this series, we will look at the key components of Safina's "brain" and "nervous system".

The Series "Insight into Safina AI"

• Part 1: The Core Architecture – Real-Time AI for Speech (You are here)

• Part 2: The Brain – Context vs. RAG for Enterprise Knowledge

• Part 3: The Senses – Multimodal Inputs with Speech-to-Text (STT)

• Part 4: The Voice – Human-Like Text-to-Speech (TTS) with Low Latency

The Challenge: Real-Time Conversations Are More Than Just Query-Answer

A web request follows a simple pattern: request, processing, response. A real-time conversation is fundamentally different. It is a continuous, bidirectional data stream where latency is not just a performance metric, but a central part of the user experience. Even a delay of a few hundred milliseconds can make an AI seem slow and unnatural. That’s why metrics like Time to First Token (TTFT) and Time to First Byte (TTFB) are crucial:

• TTFT (Time to First Token): How quickly does the AI begin to think about a response? This is critical for the perceived speed of the Large Language Model (LLM).

• TTFB (Time to First Byte): How quickly do you hear the first sound of the AI response? This measures the entire pipeline – from transcription to processing to speech synthesis.

To tackle this challenge, Safina relies on a highly integrated high-speed pipeline.

Safina's Integrated Architecture

Instead of relying on a distributed system of microservices that can cause network latency, Safina's core components – Speech-to-Text (STT), Large Language Model (LLM), and Text-to-Speech (TTS) – work in a single, highly optimized service.

This is how a conversation unfolds:

[🎙 Audioeingang (SIP-Trunk)]
            |
            v
[📝 Speech-to-Text (STT) – Transkription in Echtzeit]
            |
            v
[🧠 LLM-Verarbeitung + In-Kontext-Wissen]
            |
     +---------------+
     | Benötigt      |
     | externe       |
     | Daten?        |
     +-------+-------+
         Ja  |  Nein
         v   |   v
[📚 RAG-System]   [💬 Antwort generieren]
         \   |   /
          \  |  /
           \ | /
            \|/
[🔊 Text-to-Speech (TTS) – Sprachsynthese]
            |
            v
[📡 Audio-Streaming zurück an Anrufer]

1. Audio Recording: The live audio stream from the SIP trunk is directly fed into the service.

2. STT Processing: The audio is instantly converted to text by our STT engine.

3. LLM Processing & In-Context Knowledge: The transcribed text goes to the core LLM. Frequent and important information (e.g., business hours, standard greetings) is kept directly in the LLM's context window – for lightning-fast retrieval.

4. Data Retrieval (RAG for Large Data Sets): If you need information that is not in the immediate context – such as order details or data from a large knowledge database – the system accesses our Retrieval-Augmented Generation (RAG) system. This is the bridge to external data sources. We will look at the trade-offs between in-context storage and RAG in Part 2.

5. TTS Generation: Once the LLM formulates a response, it is directly sent to the TTS engine within the same service.

6. Audio Streaming: The TTS engine generates the audio and streams it back to you – for a fluid conversation experience.

Why This Matters for Your Business

The integrated approach offers you several advantages:

• Scalability: Each component (STT, LLM, TTS, RAG) can be independently scaled based on load. If transcription becomes the bottleneck, you only scale that service – without affecting the others.

• Resilience: If a microservice fails, it does not bring down the entire system. The architecture allows for graceful degradation and fault isolation.

• Extensibility: Crucial for dynamic business workflows. Want to integrate Safina with a local MySQL database? Or with your own ERP system? You can create new integrations that listen for data retrieval events and connect to your data sources via a secure API. The core system of Safina does not need to be redeveloped for this.

Next Part: The Brain

We have covered the "nervous system," which allows Safina to react in real-time. But what about the "brain"? How does Safina understand complex queries and access your company's specific knowledge database?

The next article will cover Part 2: The Brain – Context vs. RAG for Enterprise Knowledge. We will discuss the trade-offs between storing data in context for speed and using RAG for access to extensive knowledge databases. Stay tuned to learn how to equip your enterprise infrastructure with a truly intelligent voice.