Judge LLM: What If You Could Review Every Support Conversation?

Your support team handles thousands of conversations every month. How many of them actually get reviewed?

Manual quality assurance (QA) usually means selecting a sample of chats, checking them against a scorecard, and looking for areas to improve. It works, but there's a limitation: you only see what you've reviewed.

With Echomind's Judge LLM, you can evaluate every completed conversation against your own quality criteria, whether it was handled by a human agent or AI.

Here's how it works and what you can do with the results.

What Is Judge LLM?

Judge LLM is an AI model that analyzes support conversations after they've ended. Once you define the criteria that matter to your business, as well as how you define “high-quality support,” Judge evaluates each completed conversation based on those criteria, regardless of whether the conversation was with our AI agent or a human agent.

You create a query. You set the criteria and the value of each point. For example, the “usefulness” criterion could look like this: 10 means that the agent fully answered the customer’s question and the problem was truly resolved without any follow-up questions, 5 means that the answer was only partially helpful, and 1 means that it wasn’t helpful at all. You can define as many criteria as you like, with any number of rating levels.

Once the setup is complete, Judge takes over. The system reads the entire chat history, identifies who was involved in the conversation, and evaluates it based on each of the criteria you’ve set. There’s no sampling, no selecting chats to review. Every conversation is reviewed.

Where This Gets Used

Judge LLM has two main use cases: improving AI performance and evaluating the quality of human support.

1. Improving AI performance

Judge helps you understand where your AI agent is doing well and where it needs improvement.

For example, it might reveal that the AI handles routine questions correctly but struggles with withdrawal requests, misses important details, or escalates conversations when it could have resolved them.

Instead of manually searching through chat logs, your team can identify recurring issues, review specific conversations, and see what needs to be fixed — whether it's the knowledge base, instructions, or integrations.

2. Evaluating human support

The same approach works for conversations handled by human agents.

You can evaluate how well agents resolve customer issues, follow internal procedures, communicate with customers, and handle situations such as escalations or retention requests.

Judge helps your QA team identify conversations that need attention and spot recurring problems across the support team. This makes it easier to understand where additional training or process improvements may be needed, without relying solely on a small sample of manually reviewed chats.

What Criteria Look Like

Every business has its own definition of good customer support. With Judge LLM, you decide what to evaluate and define what each score from 0 to 10 means for a specific criterion.

Here are a few examples of how this works across different industries:

E-commerce.

A customer contacts support to cancel an order. The company wants its agents to offer an appropriate alternative before processing the cancellation, provided the customer is open to it and the company's policy allows it.

The Order Retention criterion could be configured as follows:

• 10 points: The agent offered a relevant alternative, such as a replacement, discount, or free shipping, and the customer decided to keep the order.

• 5 points: The agent attempted to retain the order but didn't explore all appropriate options.

• 2 points: The agent immediately processed the cancellation without offering an alternative, even though the situation and company policy allowed for one.

By evaluating cancellation conversations against this criterion, Judge helps the company understand whether its retention approach is being followed and where opportunities are being missed.

Fintech and Payments: Withdrawal Escalation

A customer contacts support because their withdrawal is delayed. The company has a defined procedure: the agent must check the transaction status, collect the required information, and escalate the case to the correct team if necessary.

The Withdrawal Escalation criterion could be configured as follows:

• 10 points: The agent followed the required procedure, checked the available transaction information, collected the necessary details, and escalated the case correctly when needed.

• 5 points: The agent took some of the required steps but missed important information or submitted an incomplete escalation.

• 1 points: The agent escalated the case immediately without performing the required checks or collecting the necessary information.

Judge evaluates completed withdrawal conversations against this criterion. If many chats receive low scores, the company can investigate whether agents need additional training or whether the escalation process itself needs improvement.

The evaluation is based on the information available to Judge. Verifying actions performed outside the conversation requires the relevant system data to be provided.

Telecom: Roaming Activation

A customer contacts support and asks to activate roaming before an upcoming trip. The company wants its agents to check whether roaming is available for the customer's plan, explain any applicable charges, and activate the service after receiving confirmation.

The Roaming Activation criterion could be configured as follows:

• 10 points: The agent checked roaming availability, explained the applicable charges, received the customer's confirmation, and successfully activated the service.

• 5 points: The agent provided the correct information but didn't complete the activation, even though it was possible.

• 0 points: The agent gave a generic response or failed to explain the charges and activation process.

By evaluating roaming requests against this criterion, Judge helps the company identify whether agents are following the activation procedure and completing customer requests rather than simply providing instructions.

How Echomind Can Help With It

From scores to actual improvements

Scoring conversations is only useful if you can do something with the results.

Judge LLM helps you identify which conversations need attention, where your AI agent struggles, and which support processes may need improvement.

Instead of relying only on a small sample of chats, your QA team gets a much broader view of what's happening across customer support — and can spend more time fixing problems rather than searching for them.

Want to see how Judge LLM could evaluate your support conversations? Leave us a demo request, and we'll show you how it works with criteria relevant to your business.