Unlock AI Potential
AI agents are buying products, running accounts, and signing up agreements with little to no human assistance. One of the most impactful innovations in this area is real-time voice technology.
Unlike text-based interfaces (e.g., command-line) or asynchronous APIs, voice has other constraints: low latency, synchronous interaction, and the need to respond to the user in real time during the conversation. For machine customers—AI-driven entities that conduct transactions on behalf of businesses or consumers—these requirements are foundational.
If you want to keep ahead of your competition, voice-enabled machine customer development is exactly what you need. This post will explore how real-time voice AI for transactions is altering the way machine customers interact with brands and the infrastructure that makes it possible. Understand these systems today, and you will be prepared for voice-driven autonomous transactions tomorrow.
By 2028, Gartner expects 15% of daily work decisions to be made autonomously via agentic AI, up from 0% in 2024. The goal-oriented nature of this technology will result in more flexible software systems capable of performing a wide range of tasks.
Agentic AI could deliver on CIOs’ wish to boost productivity organization-wide. This is spurring enterprises and vendors to research, experiment, and mature the technology and practices that will impart this agency in a robust, safe, and trustworthy fashion.
But why voice?
See, text is an efficient medium to use for AI interactions. However, it lacks the immediacy and flexibility that many relationship types suggest. Voice provides nuance: tone, pacing, and the possibility to clarify in real time.
For machine buyers, this matters in a few use cases:
We are not stating that real-time voice AI for transactions is about replacing text. It is about catering to the need for interaction, in which pace, clarity, and on-the-spot decision-making are essential.
Establishing a dependable voice-stack for machine consumers requires multiple, interrelated pieces. They all have a different role in making sure they can interact at the pace and quality that a fully autonomous transaction requires.
Latency is the most significant technical constraint in real-time voice commerce. For a conversation to appear responsive—and for a machine customer to take action on information in real time—end-to-end audio delay must generally remain under 300 milliseconds. Interaction quality will deteriorate beyond this limit.
Low latency is achieved through the optimization of every stage of the process, from audio capturing to encoding (audio/digital signal processing), DSP, decoding, and playing. Each layer of the stack adds delay. The aim at each and every point is to reduce it without compromising the reliability or audio quality.
Low latency for voice transaction automation platforms is possible if you:
For those developing live voice interaction systems for AI agents, LiveKit offers:
The combination of WebRTC and platforms such as LiveKit is what allows AI agents to call, listen, reason, and respond in real time.
Knowing how the technology works is one thing. Knowing where to apply it and why is what leads to adoption. The next examples show the transactions on which real-time voice AI for transactions provides the most clear-cut operational benefits.
In supply chain and procurement, machine customers are making more of the routine buying decisions on their own, with less human oversight. A voice-based procurement agent might call a supplier, check on availability, confirm pricing, and place the order — all with a single, real-time, conversational interaction.
What differentiates this from a typical API call is the ability to handle variations. Vendors could provide substitutes, shorter lead times, or discounts. A voice AI agent receives this information in real-time, applies relevant business rules, and responds dynamically. No human in the loop needed to review the interaction.
Some types of transactions require human approval before completion. In financial services, large transfers can trigger a live approval process. In medicine, a prescription order or treatment authorization may need a real-time confirmation from a licensed practitioner.
Real-time voice AI allows these workflows to be as speedy as ever without risk of compliance lapse. The machine customer prompts the interaction, provides relevant information, and routes to an appropriate human for a real-time decision. Upon receiving approval, it continues with the interaction.
The method decreases the wait time for approvals from hours (or days for workflows based on email) to a matter of minutes and at the same time maintains the audit trail and authorization controls required by regulated industries.
No automated system handles all situations perfectly. When a machine customer runs into an exception—an unknown vendor, a vague contract term, a payment dispute—the path to escalation for that issue really matters.
Voice-based escalation is faster and bears more context than text. A live voice interaction for AI agents lets the system verbally brief a human operator, transfer relevant context, and pass the interaction to the human without the need to sift through a long log.
Good escalation design considers:
Enterprises evaluating a voice transaction automation platform for machine customer applications need to evaluate a number of factors beyond just the features.
See, advertised latency figures commonly represent best-case scenarios. Actual performance when subjected to concurrent load from multiple clients (multiple processes running on a single machine or multiple machines) can be significantly different. You need to test platforms under realistic loads prior to production deployment.
A voice transaction platform is only as valuable as its integration with the rest of the AI stack. It includes the STT process, the language model inference, and the TTS synthesis. Look for platforms that provide flexible integration with best-in-class vendors. Or you can enable self-hosted models for those organizations that need to keep their data within their own walls.
Voice interactions that involve a financial transaction or a party's health information, or that contain any personally identifiable information, are regulated under several laws in most jurisdictions. Evaluate the platform’s data retention policies, encryption in transit and at rest, and logging for auditing.
Real-time voice systems are harder to debug than asynchronous text systems. Having a platform with observability tools like recording sessions, transcript logs, latency metrics, and error tracing makes for a huge reduction in time to resolution when problems happen.
Real-time voice processing is advancing quickly. Neural TTS technology has progressed to a point where the voice produced by a TTS system sounds very close to that of a human in many scenarios. STT systems now perform with near-human accuracy over accents and noise conditions. With improving hardware and model optimizations, LLM inference times are continuing to decrease.
Together, these trends are converging toward a near-future in which voice-based autonomous transactions are not the outliers but the expected. Machine customers will make routine procurement calls, escalate customer service, negotiate service renewals, and comply with workflows in real-time voice.
Those organizations that establish the infrastructure and governance for this and develop the connectivity required to integrate with other enterprise systems will be far ahead of the game when that future arrives.
Platforms based on WebRTC, like LiveKit, also provide a good foundation for teams looking to create live voice interaction with AI agents, as they are open source with an active community of developers.
The voice-powered autonomous transaction infrastructure is built today. Those that grasp it—and apply it thoughtfully—will shape the next generation of machine customer commerce.
