Voice AI Checklist: How to Evaluate Vendors Beyond Flashy Demos

While consumer preference is shifting to texting over calling, voice remains the dominant contact channel, accounting for up to 70% of customer service interactions. Voice calls aren’t just high volume; they’re also long and expensive, with average handle times of 6 minutes and 10 seconds and costs of $3-6 per call.

The combination of high cost, high volume, plus new AI technology that enables human-like conversations has created a gold rush. Dozens of vendors are entering the voice AI space, each promising to revolutionize customer service. 

But when evaluating these solutions, there’s far more to consider than what catches your eye in a flashy demo. You can think about it like an iceberg: There’s what you can see and hear, and then there’s an entire platform below it. You’ll have to ask the right questions to determine if that platform ‘below the iceberg’ is solid, or not.

In this article, we’ll cover the key considerations for evaluating a voice AI vendor built for enterprise scale, not just a flashy demo.

How to Separate the Sizzle from the Steak in a Voice Demo

When evaluating new vendors, it’s critical to separate the noise from the actual substance. What impresses in a 5-minute demo often falls apart when deployed across millions of real customer interactions.

A compelling voice demo typically features two hallmark features:

1. An extremely life-like voice that sounds just like a human.
2. Fast, friction-free conversations, like “I’ve gone ahead and booked that trip for 2 for 5 nights with dinner planned each night.” 

These are flashy and exciting, but you could create this kind of experience using ChatGPT with zero integrations, zero guardrails, and zero enterprise-readiness.

With voice and LLMs, it’s fairly easy to create a compelling demo that isn’t backed by enterprise-grade infrastructure. In fact, oftentimes these demos sacrifice enterprise best practices (like real enterprise guardrails), to make a happy path demo sound more lifelike. So if a great-sounding voice and snappy responses don’t indicate you’ve found the right vendor, what does?

What Actually Matters for Voice AI?

The following capabilities may not surface in a happy-path demo, but they’re crucial for handling real enterprise voice interactions at scale. Just ask Spirit Airlines.

1. Multimodal Communication

Voice is great in many ways, but it’s terrible for certain tasks. Try reading five flight options with times, prices, and layovers out loud, and asking someone to remember them all.

The best voice AI solutions understand that voice should be augmented, not isolated. Customers should be able to:

• Receive complex information via text while staying on the same conversation (flight options, order confirmations, tracking links).
• Respond via text when it makes sense (spelling a name, entering an account number, securely logging in to verify identity, selecting from a list ).
• Shift channels without losing context (share a photo for a warranty claim, continue later, etc.).

Here’s an example of how we do this at Quiq for our customer, Spirit Airlines:

💡What to look for: Native multimodal support that keeps voice and messaging in a single conversation thread, with intelligent channel switching and consistent AI behavior across channels.

Why this matters: Most voice AI vendors offer voice in a silo from other channels, which limits use case complexity. Multimodal capabilities expand what you can automate while improving the customer experience, driving higher resolution and higher CSAT.

2. Technical Performance

Before you can build compelling voice experiences, you need solid technical infrastructure. Without it, even the smartest AI will feel slow, unreliable, and frustrating to customers. There are a few core areas to consider:

Latency

Every delay is amplified on voice. Slow responses (from the text-to-speech and speech-to-text models, the LLMs, or API calls) feel interminable on a phone call. 

💡What to look for:

• Model-agnostic architecture for TTS, STT, and LLMs, so you can swap to faster or more performant models as they emerge, and try out different models to find what works best for your experience as it evolves. You don’t want to get stuck with outdated models because your vendor has locked you into a single provider.
• Parallel processing that can start API calls, or other long-running tasks, early in the conversation flow. For example, fetch flight details the moment a customer authenticates, before they even ask about rebooking, so they’re not stuck waiting when they try to rebook.
• Flexible model usage that lets you run multiple prompts in parallel, leverage multiple models through your experience, including your own, speeding up response time and accuracy of your voice agent.

Uptime and Resiliency

Outages and performance degradation can happen. When a service fails, what happens?

💡What to look for:

• Proven uptime SLAs for core infrastructure.
• Automatic fallbacks at every layer (if one STT provider fails, seamlessly switch to another).
• Graceful error handling with configurable retries and escalation paths.

Accuracy

Great voice AI is useless if it can’t understand your customers.

💡What to look for:

• Best-in-class STT & TTS models with flexibility to vary by region and language, within the same agent.
• Sophisticated disambiguation that handles garbled speech, interruptions, and clarifications.
• Accent and dialect support that works for your customer base in all markets.

Why this matters: Technical performance is the foundation everything else is built on. Without low latency, your AI feels robotic and frustrating regardless of how smart it is. Without resilience, a single service outage takes down your entire customer service operation. Without accuracy, customers repeat themselves endlessly or give up in frustration. Unreliable performance will kill adoption faster than any other factor.

3. Agentic Architecture

Once you’ve got the above pre-requisite foundation in place, how do you actually build a compelling experience? 

Well, you don’t want to go through all that work, only to end up deploying a simple chatbot that just answers questions from knowledge and escalates to a human agent. Therefore, it’s important to pick a vendor who can help you truly build agentic experiences on voice.

What does agentic actually mean?

• Your AI agent should follow Process Guides like your best human agent would, adapting to the conversation naturally, rather than forcing customers down rigid paths.
• The system should reason across the entire conversation, maintaining context and adjusting its approach based on detected customer sentiment, data from external systems, like your CRM, conversation history, and much more.
• It should proactively solve problems, not just answer questions. For example, it might identify that a customer is calling about a delayed flight and automatically offer rebooking options.
• It needs customizable guardrails that enable it to handle the longtail of customer requests and non-happy path scenarios, while ensuring every interaction is brand approved, accurate, and helpful.

💡What to look for: An AI platform built for deploying agentic experiences, with Process Guides or equivalent architecture, enterprise integration capabilities capabilities, strong context management, configurable guardrails, and the ability to integrate business logic into the experience.

Why this matters: This is the difference between 10% call deflection and 60+% call deflection. Question-answering bots create minimal value, as customers often find that information themselves on your website or app. Task completion is what actually reduces escalation volume, cuts costs, and improves customer satisfaction. It’s also what justifies the investment in voice AI. If your AI can’t complete common multistep tasks autonomously, you’re essentially building an expensive routing system that still requires the same number of human agents.

4. Enterprise Readiness

It’s impossible to feel confident in an agentic AI experience if it’s a black box you can’t understand or control. Enterprise voice AI requires robust tools for testing and debugging, the ability to deploy consistently across all customer touchpoints, and the right balance of autonomy and vendor support. You’ll need to continuously iterate based on real customer interactions, and you can’t afford to wait weeks for vendor or engineering resources every time you need to make a change.

Testing and Observability

Building agentic AI is complex. You need tools to understand why your agent behaved in a certain way.

💡What to look for:

• Full event visibility showing the complete reasoning chain for every interaction, not just “what answer did the agent give?”
• Reusable test sets to quickly identify regressions and understand how your agent reacts to changes, without needing to test in production.
• Version control and rollback with zero downtime.
• Real-time debugging for issues as they happen, not weeks later in a dashboard.
• Deployment and iteration: Can business users make updates, or does every change require engineering or vendor support?
• Fully customizable Insights & Analytics: Your voice AI shouldn’t be hamstrung by simple out of the box reporting. Make sure you pick a vendor that allows the customization and flexibility you need to measure what matters.

Consistent Brand Experience

Your customers don’t think in channels. They just want help. A customer might start on web chat, call in later, then text a question. Fragmented AI experiences across channels create confusion and frustration. Your AI provider should connect CX across channels.

💡What to look for:

• Omnichannel deployment that lets you build once and deploy the same agent across voice, web chat, SMS, WhatsApp, Apple Messages for Business, email, and more.
• Seamless channel switching so customers can move from voice to WhatsApp when they get home, or from web chat to SMS when they need to send a photo—without repeating themselves or losing context.
• One agentic architecture that shares the same knowledge base, guardrails, and business logic across every channel.

Control and Partnership

Enterprise voice AI requires both autonomy and support. You need the ability to make changes yourself without waiting on vendor resources, while also having access to deep expertise when you hit complex scenarios.

💡What to look for:

• No-code configuration so your CX team can update responses, adjust flows, and refine the experience without engineering dependencies.
• In-house professional services team with deep experience in your industry and use cases, not just generic chatbot builders.
• Flexible engagement models that let you self-serve for minor updates, but provide white-glove support for complex integrations and strategic deployments

Why this matters: Voice AI isn’t a “set it and forget it” solution. Customer needs change, your products evolve, edge cases emerge, and you’ll need to iterate constantly. If every change requires engineering sprints and weeks of development time, your AI will become stale and frustrating. Enterprise-ready platforms enable your CX teams to own the experience, test changes safely, understand why issues occur, and fix them quickly. This is the difference between AI that improves over time and AI that slowly degrades as it falls out of sync with your business reality.

5. Integrations

Voice AI requires deep integration with your telephony system, CRM, order management systems, and more. But integration isn’t just about connecting systems, it’s about making your data actually usable by AI. These integrations are rarely plug-and-play, so the vendor’s integration experience and CX expertise matters more than their integration claims.

💡What to look for:

System Connectivity

• Can the platform integrate with diverse systems through standard protocols?
• Telephony specifics: How does it work with your phone system? How do handoffs to humans work?
• Does the vendor have experience with your specific tech stack?

Data Transformation Capabilities

Your systems likely store data in formats optimized for humans or other software, not AI. Customer records might be spread across multiple tables. Product catalogs might use internal codes. Order statuses might be cryptic abbreviations.

• The vendor should handle transforming disparate data sources (product catalogs, knowledge bases, how-to manuals, etc.) into formats the AI can reason about, while keeping transformations in sync with the original sources so you’re not stuck managing multiple sources of truth.
• Look for AI-powered transformation capabilities that can enrich your data (adding clearer descriptions, identifying what questions an article answers), clean it (removing irrelevant contact info or social links), and reformat it (stripping markdown, improving structure) automatically.

Implementation Support

• Does the vendor have experienced teams who’ve integrated with similar tech stacks?
• Can they handle not just connecting to your APIs, but understanding your data model and transforming it appropriately?
• Do they understand how to design an enterprise voice AI, not just build a flashy demo?

Why this matters: Integration complexity is where AI projects often stall. But it’s not just about making API calls and integrating with external systems. Your AI needs clean, contextualized data to make good decisions. Vendors with deep AI, CX, and integration experience can accurately scope work, handle data complexity, and easily manage the unanticipated (a hallmark of true agentic AI). The difference between 6 weeks and 6 months often comes down to whether they’ve solved these problems before, and whether they can turn your messy real-world data into something AI can use effectively.

In Conclusion

There’s a lot to evaluate beneath the surface of flashy voice AI demos. But the payoff is substantial. Companies deploying enterprise-grade voice AI see double-digit CSAT gains when voice AI is deployed, reduce costs by 50%, and free up their best human agents to handle complex, high-value cases that genuinely require human judgment and empathy.

The gap between a compelling demo and an enterprise-ready voice AI system is wide. By focusing on the capabilities that matter, you can separate vendors building for the long haul from those just riding the hype cycle.

Frequently Asked Questions (FAQs)