Why You Can’t Build Live AI Agents on Borrowed Transcription

Nowhere is this more apparent than in voice interactions.

In customer service, performance is not measured in seconds but in milliseconds. Delays that seem minor on paper can become noticeable in live conversations, affecting both customer perception and agent confidence.

As a result, real-time responsiveness has become a critical factor in CX, and many Voice AI solutions struggle to maintain performance outside controlled demonstrations.

Why Real-Time CX Breaks on Third-Party Transcription

Most Voice AI systems today are assembled as pipelines. Audio is captured, sent to a third-party transcription service, returned as text, forwarded to another system for analysis, and only then used to drive guidance or responses.

• Each step introduces delay
• Each API call adds overhead
• Each dependency increases fragility

Individually, these delays might appear acceptable. Together, they compound into a very perceptible >600ms delay.

In a live conversation, milliseconds are the difference between something that feels natural and something that feels broken.

This is the same architectural issue we discussed earlier in the series. When intelligence is stitched together instead of designed as one system, performance suffers. Voice simply exposes the problem more brutally than any other channel.

Why Live AI Requires a Different Architecture

Live Agent Assist and Virtual Agents operate under constraints that batch systems never face.

Customers pause mid-sentence.

• They interrupt themselves.
• They speak slowly when reading numbers.
• They speed up when frustrated.

Silence carries meaning.

A system that waits for clean sentence boundaries or complete utterances is already behind the conversation.

This is why Level AI built its own Voice AI engine instead of relying on borrowed transcription. By owning the speech layer and integrating it directly with downstream intelligence, we remove unnecessary hops and reduce end-to-end latency.

The goal is not just faster transcription. The goal is faster understanding.

Consistency Across the Stack Is Not Optional

Latency is only part of the problem. Inconsistency is the quieter failure mode.

When different parts of the CX stack rely on different transcription systems, the same conversation can be interpreted differently depending on where it appears. Live agent assist sees one version. Post-call QA sees another. Analytics operates on a third.

That inconsistency fractures learning.

• Agents lose trust in guidance.
• QA flags issues that automation does not recognize.
• Models are trained on mismatched inputs.

By using a single, unified speech model across real-time and post-interaction workflows, the system maintains one version of the truth.

The same words trigger the same intents. The same phrases are evaluated the same way. Intelligence stays aligned across humans and AI.

As we argued earlier in this series, learning only works when the system learns as one.

Accuracy in Real-World CX Environments

Contact centres are not controlled environments. Accents vary widely. Background noise is constant. Industry-specific terminology is common. Customers speak emotionally, quickly, and often imprecisely.

Generic transcription models are designed to be broadly useful. They perform well in ideal conditions, but struggle in the chaos of real CX.

Owning the ASR layer allows for targeted improvements that compound across the system:

• Better handling of accents and noisy environments
• Support for domain-specific vocabulary without artificial limits
• Consistent transcription quality across live and batch workflows

When speech is the foundation for quality automation, analytics, and virtual agents, these improvements matter far beyond transcription accuracy alone.

Why “Please Wait” Is an Architectural Smell

One of the clearest signals of a slow Voice AI system is the phrase customers hear far too often: “Please wait while I process that.”

That pause is not a UX choice. It is an architectural constraint.

True live AI agents should be able to listen without interrupting at the wrong moment, recognize intentional pauses, and respond immediately when the customer finishes speaking. They should adapt dynamically as context shifts.

These capabilities are impossible when perception and intelligence are split across systems that were never designed to operate together in real time.

Owning the stack creates the flexibility to evolve toward this future. It allows models to be trained not just on words, but on conversational rhythm and intent.

Voice Makes the Case for Unified Intelligence

Voice is where fragmented architectures break first. It is also where unified systems prove their value most clearly.

By owning the Voice AI engine, Level AI ensures that improvements in speech understanding benefit every surface of the platform.

Live Agent Assist reacts at conversational speed. Virtual Agents feel responsive instead of robotic. QA, analytics, and automation operate on consistent inputs.

This is the same principle we have reinforced throughout this series. Purpose-built models, unified architecture, and shared learning loops are not independent decisions. They are requirements for AI that works at enterprise CX scale.

This blog post has been re-published by kind permission of Level AI – View the Original Article

For more information about Level AI - visit the Level AI Website

Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: Level AI
Reviewed by: Jo Robinson