Quality Control at Palona: What It Means for Your Restaurant

Quality Control at Palona: What It Means for Your Restaurant

When you put AI in front of your guests, you are trusting it with more than a conversation. You are trusting it with your menu, your orders, your team’s time, and your restaurant’s reputation.

A guest’s request for a different side needs to reach the order correctly. A change in quantity needs to be carried through. A caller who needs help from your team needs an appropriate next step.

For Palona, quality means looking beyond how natural the AI sounds to understand whether it is doing the job your restaurant needs.

Daily evaluation, not just a successful demo

Our quality-control process includes daily automated evaluations and performance tracking at both the individual restaurant deployment and customer-account level. This helps us identify where the AI is performing well, where outcomes are changing, and which experiences need closer investigation.

For an operator, a companywide average is not enough. You need to understand what is happening in your business. We examine differences between deployments, when guests are calling, and the kinds of conversations associated with quality issues. That evidence helps us prioritize reviews and focus quality checks where they matter, including busy service periods and more complex orders.

Checking the details your kitchen depends on

An order can sound correct during the conversation and still contain a mistake when submitted to the restaurant’s point-of-sale system.

Our call investigations compare what the guest requested, what the AI repeated back, and what was ultimately submitted. This helps us distinguish between a detail lost during the conversation and a detail incorrectly translated into the order. Those are different problems that require different fixes.

For your restaurant, the distinction is practical: the kitchen works from the order it receives, not the conversation that came before it. Quality review needs to follow the guest’s instructions through that process.

We also investigate changes made after an order has already been submitted. Recognizing a new request is not the same as successfully updating an existing order. Our reviews identify these situations separately so they can inform the right product improvements.

Adding structured human assessment

We are strengthening this process with structured human assessment testing alongside automated evaluation.

The purpose is to bring human judgment into how we assess the experience, not just the technical outcome. For a restaurant operator, the question is straightforward: would this interaction meet the standard of clarity, care, and helpfulness you expect from your own team?

Automated evaluation and human assessment should complement each other. The goal is to understand both whether the task was completed and whether the experience was appropriate for the guest.

Measuring outcomes that matter to operators

Our quality scorecard separates several outcomes that can otherwise get grouped together under a vague claim that AI “handled the call.”

Order conversion: How often calls from guests who intend to order show evidence of order creation. This helps identify where ordering demand is being captured and where the ordering journey needs attention.

Order accuracy: Whether evaluated orders match the guest’s request, including order content and fulfillment details. Creating an order and creating the right order are separate measures.

AI job completion: Whether the AI is assessed to have completed the caller’s task. A conversation ending without a transfer does not, by itself, establish that the guest got what they needed.

Upsell AOV: How guest-accepted recommendations, such as adding a side, drink, or dessert, contribute to average order value. For your restaurant, the goal is to grow the check through helpful, relevant suggestions while preserving an accurate order and a positive guest experience.

We also keep the limits of these measures visible. Order creation does not establish payment or fulfillment. A recorded transfer does not confirm that a staff member answered. An interaction without an assessment is not automatically counted as a success. These distinctions help keep performance reporting grounded in what the evidence actually shows.

Turning findings into tested improvements

Identifying an issue is the beginning of improvement, not the end.

Our process connects quality findings to focused experiments: identify a gap, test a change, measure the outcome, and use the evidence to inform rollout decisions. One example is our live speech-recognition A/B pilot, which examines how changes affect understanding of callers, restaurant terminology, and guest-experience signals. Where findings remain inconclusive, we continue gathering evidence rather than treating an early result as proof.

For restaurant operators, that is the commitment behind our quality-control work: ongoing evaluation, detailed investigation, human judgment, and evidence-based improvement.

Our goal is to help your restaurant capture more revenue while protecting the accuracy, service, and guest trust that make that growth valuable.

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