Automating Call Centers with AI Agents: Achieving 700ms Latency

Ihor Hamal — Wed, 21 May 2025 08:46:40 +0000

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.

Forem: Ihor Hamal

Automating Call Centers with AI Agents: Achieving 700ms Latency