Automating customer support with AI-driven agents fundamentally involves integrating Speech-to-Text (STT), Large Language Models (LLM), and Text-to-Speech (TTS). However, simply plugging these models together using their standard APIs typically results in high latency, often 2-3 seconds, which is inadequate for smooth, human-like interactions. After three years of deep-diving into call-center automation in SapientPro, I've identified several crucial strategies that reduce latency to below 700ms, delivering near-human conversational speed.
Understanding the Workflow
To automate a call center effectively, three main components must collaborate seamlessly:
Speech-to-Text (STT): Converts audio into textual data. Popular models include Whisper and Deepgram.
Large Language Models (LLM): Processes the textual input to generate appropriate conversational responses. Common choices include OpenAI's GPT, Google Gemini, Anthropic's Claude, Meta's Llama, and Mistral.
Text-to-Speech (TTS): Converts generated textual responses back into audio. Typical providers are ElevenLabs and PlayHT.
The Problem with Typical Implementation
If you simply connect these components via standard REST APIs, you’ll encounter cumulative latency issues:
STT Processing: Waiting for full sentence transcription (~1 second).
LLM Processing: Sending transcribed text via REST APIs, incurring network latency (~1 second).
TTS Processing: Additional REST API calls to synthesize audio (~500ms-1 second).
This straightforward integration inevitably leads to unacceptable latency of around 2-3 seconds per interaction.
Optimizing Latency: Essential Techniques
To drastically reduce latency, implement the following best practices:
- WebSockets Over REST APIs
REST APIs require waiting for the complete transcription before processing can start. Instead, use WebSockets to stream audio-to-text conversions:
Real-time streaming: Providers like Deepgram support WebSocket connections that deliver transcriptions word-by-word.
Immediate processing: You can send partial transcriptions to your LLM instantly, saving approximately 1 second per interaction.
- Dedicated LLM Infrastructure
Public APIs (like OpenAI’s public ChatGPT) suffer from variable performance based on external load. To ensure consistent latency:
Azure ChatGPT Instances: Azure offers dedicated ChatGPT infrastructure, isolating your LLM from public traffic fluctuations, significantly reducing latency variability.
Alternative Hosting: Consider privately-hosted LLMs (e.g., Llama, Mistral) optimized specifically for your workload.
- Local Hosting of AI Components
Co-location of your STT, LLM, and TTS models within the same local infrastructure drastically reduces network overhead:
Local Deployment: Host Whisper or Deepgram STT locally. Deepgram provides self-hosted solutions specifically designed for low latency.
Unified Infrastructure: Run TTS models like ElevenLabs or PlayHT within your internal network infrastructure.
Hosting these components on a unified, optimized infrastructure allows near-instantaneous internal communication, eliminating external network delays.
Achieving Human-like Latency
Implementing these strategies consistently results in response times below 700ms, closely mimicking human conversational speed. With this level of optimization, users often cannot distinguish AI agents from human operators based solely on response speed. The result is a natural, efficient, and satisfying customer interaction experience.
By leveraging WebSockets, dedicated or locally hosted LLMs, and unified infrastructure for all AI components, your call center can achieve a seamless and responsive AI-powered conversational experience.
Top comments (7)
pretty cool seeing all the nuts and bolts here - honestly keeping that response time so low is brutal, do you think there's ever a real ceiling for how 'human' ai can sound or will it always come down to new tricks and hardware?
I believe that the way ElevenLabs sounds atm is already difficult to distinguish from a human voice. The only technical limitation at this point is background noise.
For example, if you make a call in a noisy environment, such as with a tv playing or in a crowded place, or in a restaurant where multiple people are talking, the STT system might capture words from different voices in the background. This often results in a confusing mix of unrelated speech, which actually could be quite funny.
That's super interesting! I’m wondering how important the choice of specific tools (like Whisper or GPT) is for reaching that low latency. Is it more about how they’re connected and optimized?
Great question. I would recommend using Deepgram together with Azure GPT, as this combination is currently among the most effective. Deepgram is particularly useful because it supports extended WebSocket functionality, which can offer greater performance.
However, the ideal choice depends significantly on your specific objectives and requirements.
Thank you for the answer. Will think about this.
Open-source models are a great option too — especially if you're aiming for really low latency. Running them locally can be a bit more work to set up and maintain, but the boost in performance is often totally worth it.
try Soniox with "manual finalization" to lower STT latency. it's a single multilingual real time speech AI model and very accurate in all 60 languages. soniox.com/docs/speech-to-text/cor...