The audience may expect quick answers from the voice AI agent once they ask a question. In such cases, long delays after the caller’s sentence may leave them confused as to whether the call ended or the system was unable to comprehend the information.
The audience may expect quick answers from the voice AI agent once they ask a question. In such cases, long delays after the caller’s sentence may leave them confused as to whether the call ended or the system was unable to comprehend the information.
Therefore, voice AI latency plays a significant role. A voice AI agent who can respond in the timespan of 500 milliseconds would appear more interactive compared to the one with a gap of a few seconds.
However, what is meant by having voice AI with a lag of under 500 milliseconds? This answer depends on more than just a single number of latencies like recognition of the speaker’s voice, audio streaming, processing, text-to-speech conversion, etc.
All these contribute to the delay that exists between a caller finishing his/her speech and hearing a response from an agent. Knowing these factors allows businesses to set realistic expectations when assessing technology.
How Does Difference in Milliseconds Affect a Live Call?
Different latency levels may look negligible in a conversation latency benchmark, but on a real call, even a small delay can be noticeable when a customer is waiting for a response.
| Estimated Lag Time | What the caller may experience |
|---|---|
| 1.5s+ | It may occur to callers that the system lacks the ability to interpret their commands and/or that there may be errors. |
| 800ms-1.5s | The pause becomes increasingly obvious and makes the system appear slow, especially in situations in which quick back-and-forth conversations take place. |
| 500-800ms | Noticeable delay, particularly during short exchanges. |
| 300-500ms | A short pause that can feel reasonably responsive in a normal conversation. |
| Under 300ms | The response can feel very quick, with a little noticeable gap between the caller finishing and the AI responding. |
Why Does Latency Matter to Customer Experience?
When the caller starts talking to the voice AI agent, they expect the conversation to move forward naturally. In case the AI takes time to respond, even the most trivial dialogues become disconnected. Then the client may have some doubts as to whether the AI got the message or not and ask the question again while the system processes the message.
Let’s consider an example.
“I want to know where my order is”
Then you wait.
If the AI gives the answer instantly, “Yes, your order is out for delivery and it should arrive today,” then it looks like an efficient and simple response and you may not even pay attention to the speed of the answer.
Now imagine the same call with a two-second pause.
A caller asks the question and hears nothing.
Then there is some pause.
And he/she thinks, “Did it understand me?”
Maybe he/she asks, “Hello, do you understand what I said?”
Finally, the AI responds, “Your order is delivered.”
In such situations, the issue is not the wrong information provided by AI. It is the latencies that cause disconnection in the conversation. When there is too much silence between turns, even a capable AI agent can start to feel slow. This becomes even more noticeable when callers ask short questions or have back-and-forth conversations.
For example, a customer calls a clinic and says:
“I would like to reschedule my appointment for Friday afternoon.”
The AI may need to understand the request, check the patient’s appointment, look for available slots, contact the scheduling system, and then provide all the available options. If each stage adds delay while all of this happens, callers can easily feel agitated.
This is why voice agent response time is not just a technical measurement. It plays an important role in keeping conversation moving naturally and helping callers feel confident throughout the interaction.
How is Voice AI Latency Built Up During a Call?
Behind a simple question to a voice AI agent, several steps happen in quick succession before the caller hears a response:
1. Identifying When a Caller Stops Talking
People frequently do not speak precisely or coherently. A caller pauses briefly while thinking, says “um,” or takes a moment before continuity. If the system waits too long to make sure the caller is finished, the response is delayed. If it responds too quickly, it may interrupt the caller.
2. Turning Speech into Text
Using speech-to-text technology, agents translate the call into a text. However, background noise, poor call quality, bad pronunciation, and overlapped speech may make it harder for voice AI agent to go through this step.
3. Processing the Request
During calls when a caller makes a request, “I need to move my appointment to Friday afternoon. Can you check what times are available?” The AI comprehends what has been asked and decides how it will act to accomplish this aim.
4. Calling External Systems or Tools
In some cases, there might be no way of answering the question with the information that the AI has in its knowledge base at the time. For example, if the customer asks, "Is my order shipped?", then the AI might need to refer to the company’s CRM or order management systems. The latter is required to gather the necessary information, which takes more time.
5. Generating the Response
Now that the AI has all the necessary data, it provides the answer “Yes, your order was shipped this morning and is supposed to reach you tomorrow." The system doesn’t necessarily have to wait for the completion of the whole answer. Through streamlining, it can start reading the beginning of the answer while it is generating the rest of it. In such a way, the pause between the end of the caller’s question and the beginning of the answer is shortened.
6. Conversion of the Answer into Speech
To turn the text of a response into a spoken answer, AI prefers text-to-speech programming. However, it also adds to the latency because the audio must be created before the caller can hear the response. For example, the AI may generate the text “Your order is out for delivery,” but the caller hears that response only after the text has been converted into audio and delivered through the call.
7. Sending the Audio Back to the Caller
While the generated audio travels back to the caller, several factors, such as network conditions, audio streamlining, and call infrastructure can affect how quickly the response is delivered. Even when the AI generates the response quickly, delay at this state adds to the noticeable pause before the caller hears the agent.
What is TTFB in Voice AI and Why Does it Matter?
TTFB (Time to First Byte) in voice AI, helps indicate how quickly the underlying system begins returning a response. However, TTFB is not the same as the time it takes for a caller to actually hear the AI speak.
For example, a voice AI system may receive the first response data in 200 milliseconds. The system may still need to generate the spoken audio and stream it back to the caller before the first worlds are heard. This is why Time to First Audio (TTFA) can be more relevant when measuring the caller’s experience.
In simple terms, TTFB voice AI shows you how quickly the system starts returning a response, while TTFA tells how quickly the caller actually hears the AI response. This makes it important for businesses to look at TTFB alongside TTFA and overall voice AI latency. This combination of measures makes it easier to evaluate how responsive the system actually is from a technical standpoint.
Conclusion
In the case of enterprises using voice AI technology, the objective must not only be to come up with a low latency figure; instead, it must be to ensure that the conversation is smooth for the caller.
This is where GirikCTI can help businesses manage calls and continue conversations without making callers repeat information or wait unnecessarily. By combining the capabilities of voice AI and contact center, businesses can focus more on how quickly the system responds and how smoothly the entire customer interaction moves from the first word to the final resolution.
