Voice AI and Enterprise. Transforming voice into a reliable solution

‍

In Voice AI, latency is the "silent killer" of trust. Exceeding the 2-3 second wait threshold breaks the illusion of conversation. But while understanding why speed is fundamental is intuitive, understanding how to achieve it on an Enterprise scale is a complex engineering challenge.

An effective Voice Agent, in fact, is not a simple stage demo where conditions are controlled. It is a real-time pipeline that must remain stable and coherent in conditions that are far from ideal, such as background noise, overlapping speech, unstable connectivity, and sudden traffic spikes.

Beyond component assembly. The "waterfall" problem

The most common error is thinking that assembling the best components on the market, an excellent Speech-to-Text (STT) and an excellent Text-to-Speech (TTS), is enough to obtain a good assistant. The reality is that in a traditional "waterfall" architecture, where each component waits for the previous one to finish its work, the times add up, creating unacceptable voids.

The real bottleneck is not in the individual models, but in the orchestration, the way data flows and decisions are made. Let's explore how the most advanced software architectures address these technical challenges.

Proprietary orchestration

To reduce latency and make the experience truly natural, you need something that goes beyond simply combining best-in-class components. In enterprise solutions, the winning strategy is to build a proprietary orchestration and control layer, rather than relying on standardized external logic. This layer governs the end-to-end interaction, deciding when to activate each capability and how to make it work in concert with the others, optimizing the conversation with measurable precision.

Here are the key innovations that an advanced orchestration system enables.

1. Adaptive VAD (Voice Activity Detection)

Understanding when the user has truly finished speaking is a subtle art. A pause can indicate the end of a thought, but also a simple breath or a momentary hesitation. A rigid system risks two fatal errors - interrupting the user while they are still thinking (being aggressive) or waiting too long in silence (being slow). The most evolved systems dynamically balance these thresholds to adapt to the interlocutor's specific rhythm.

2. Streaming and pipeline architecture

To eliminate dead time, modern architectures do not wait for the AI's response to be complete to start generating audio. As soon as the model produces a fragment of complete meaning, this is immediately routed to the TTS. While the user listens to the first words, the system is already calculating and synthesizing the rest of the sentence.

3. Interruption management (Barge-in)

In reality, people interrupt each other continuously. A mature Voice AI must have "ears always open," even while speaking. If the user intervenes, the system must be able to

1. Detect the interruption in a few milliseconds.
2. Immediately stop the outgoing audio.
3. Reprocess the context to understand if the user has changed the subject or just added a detail.

4. Acoustic feedback and "fillers"

When the system must perform complex operations (e.g., querying a CRM), silence is the enemy of User Experience. The introduction of short signals, confirmations, or conversational fillers ("I'm checking your request, give me just a second...") keeps the channel alive. This simple measure reassures the user and drastically reduces call abandonment.

Elixir. An architecture designed for real-time

Managing voice credibly means managing real-time systems with thousands of simultaneous sessions, concurrent events, unpredictable spikes, and external integrations that are not always deterministic. In this context, it is not sufficient to "scale" by adding resources; a runtime natively designed for concurrency, resilience, and service continuity is needed.

For this reason, many of the most robust Voice AI platforms are built in Elixir, on the Erlang/BEAM ecosystem, born specifically for telecommunications, a domain where latency, availability, and fault tolerance are non-negotiable requirements. The BEAM offers an operating model particularly suited to Voice AI, lightweight processes, fault isolation, supervision, and stability under load that allows for predictable performance even in complex conditions.

What does this technological choice enable, specifically?

Parallel Execution

In a modern voice conversation, the response rarely depends on a single step. It involves retrieval from knowledge bases, calls to transactional systems, policy checks, security evaluations, logging, and tracing. In Elixir, it is possible to orchestrate these activities in parallel without blocking the main pipeline, reducing overall latency. Typical examples are the simultaneous launch of retrieval and policy checks or concurrent tool calling on multiple systems.

Semantic caching (controlled reuse)

In customer care contexts, many requests are recurring and semantically equivalent even if formulated differently. Traditional "string-based" caching is not enough. An approach is needed that recognizes equivalences and allows for the controlled reuse of outputs and intermediate steps. This enables a reduction in response times, cost containment, and greater consistency in responses.

Context-driven prefetching

In some scenarios, it is possible to anticipate part of the work while the user is still speaking. One does not "guess" the answer, but prepares probable processing branches (for example, starting retrieval or preparing queries) to compress time when the decisive information arrives. It is an approach that must be adopted with conservative criteria because, in voice, correctness counts as much as speed.

The technological roadmap. Towards native Voice-to-Voice

The market direction is set. If today the standard of excellence is reached by best orchestrating distinct components ($STT \rightarrow LLM \rightarrow TTS$), the near future belongs to native Voice-to-Voice models.

In this new paradigm, AI does not need to convert sound into text to "think" and then reconvert it into sound. Processing happens directly on the audio signal (or through multimodal tokens). This technological leap will definitively eliminate the "lossy" compression of text transcription, allowing the AI to understand and replicate not just what is said, but how it is said (sarcasm, urgency, hesitation).

This technological unlock will enable radically new use cases.

Hyper-reactivity

Almost instant turn-taking, with response times dropping below the threshold of human perception, making the interaction indistinguishable from a real phone call.

The silent Agent (Agent assist)

An AI that doesn't necessarily have to "take the stage." Imagine a system that remains in passive listening during a complex conversation between two humans (e.g., a consultant and a client). The AI analyzes the dialogue and intervenes visually on the operator's screen only to provide crucial data or suggest the next-best-action.

Security and control. Non-negotiable requirements in the enterprise scope

The faster and more autonomous a voice Agent becomes, the more stringent the governance mechanisms must be. Speed is not an isolated vanity metric; it is just one component of an ecosystem that must remain, at every instant, observable, controllable, and auditable. In an Enterprise context, the unpredictability typical of generative models must be harnessed through four pillars of governance.

1. End-to-end observability & tracing

When a delay occurs, we cannot guess. It is necessary to use distributed tracing systems to isolate the origin of latency with surgical precision (STT model, LLM, network, etc.). Without this granular visibility, optimization is impossible.

2. Guardrails and deterministic policies

We cannot leave the LLM free to improvise on critical themes. Implementing security "rails" filters inputs and outputs in real-time, guaranteeing adherence to company policies and blocking hallucinations or sensitive topics before they reach the user.

3. Evals and continuous monitoring

An AI system is not static. It is fundamental to constantly monitor the quality of responses to detect regressions or drift phenomena, testing system robustness against unexpected inputs.

4. "Privacy-aware" design

In regulated sectors like Banking & Insurance, data management is sacred. A solid infrastructure must be designed for data minimization, applying sensitive data masking techniques (PII Redaction) and rigorously controlling which information enters and leaves the secure perimeter.

‍

Designing an infrastructure for voice means managing critical real-time systems where "scaling" doesn't just mean adding resources, but guaranteeing resilience and service continuity.

Looking to the future, the road is paved toward native Voice-to-Voice models. However, even as models evolve, governance requirements will not change. In the Enterprise scope, speed can never be a metric disconnected from security. Whether it is handling user barge-in or blocking a model hallucination, technology must remain observable and controllable. Because only when engineering is solid enough to make complexity invisible does the interaction truly become natural.

FAQ

Why isn't assembling the best STT and TTS models enough to eliminate latency?

Because in a traditional "waterfall" architecture, execution times add up mathematically. Speech-to-Text must finish before the language model begins, which must finish before voice synthesis starts. To cut latency, proprietary orchestration is needed to manage streaming flows, generating audio and starting to respond as soon as it has a fragment of complete meaning.

Why is the technological choice of Elixir strategic for Voice AI?

Elixir is built on the Erlang/BEAM ecosystem, born specifically for telecommunications, a sector where latency, availability, and fault tolerance are non-negotiable requirements. This technology allows for managing thousands of simultaneous sessions and orchestrating activities in parallel (like retrieval and policy checks) without blocking the main pipeline.

What are "native Voice-to-Voice" models and how will they change the market?

Voice-to-Voice models represent the near future. The AI processes the audio signal (or multimodal tokens) directly without having to convert it into text first. This will definitely eliminate the "lossy" compression of transcription, allowing the AI to understand and replicate not just what is said, but how it is said (sarcasm, urgency, hesitation), enabling reaction times below the threshold of human perception.