A practical guide to Zendesk entity detection: Features, limits, and alternatives

If you’re running a support team today, you’re probably feeling the squeeze. Ticket queues are getting longer, customers expect answers yesterday, and the pressure is on to automate something, anything, to keep up.

This push for smarter automation is what makes features like entity detection so appealing. Zendesk has its own version, baked into its AI suite, called Zendesk entity detection. On paper, it looks like a great tool for automatically sorting and sending tickets where they need to go. But how does it actually perform when things get messy?

Let’s take an honest look at what this feature does, how you set it up, where it tends to fall over, and how newer, more flexible AI tools can give you better results without all the manual upkeep.

What is Zendesk entity detection?

Zendesk entity detection is a feature within its Intelligent Triage tool, which is part of the larger Zendesk AI package. Its main job is pretty straightforward: to automatically find and extract specific bits of information from incoming tickets.

Think of it like a smart highlighter that scans a customer's message for things like product names, order numbers, or specific reasons for contact, any unique data point that matters to your business.

Screenshot of Zendesk Advanced AI Entity Extraction, a key feature of Zendesk entity detection.

The whole system is built on connecting these "entities" to custom ticket fields you've already created in Zendesk, specifically drop-down, multi-select, or Regex (Regular Expression) fields. The idea is to use that extracted data to kick off automated workflows, like routing a ticket to the right team or bumping up its priority. It’s a step toward making your helpdesk feel a little less chaotic.

How to set up and manage Zendesk entity detection

Getting entity detection running in Zendesk isn't exactly a one-click affair. It offers some control, but the setup is manual and requires a bit of babysitting to prevent it from creating more work than it saves.

The foundation of Zendesk entity detection: Creating entities from custom fields

First thing’s first: you can’t just invent entities on the spot. They have to be tied to custom ticket fields. This means your first stop is your admin panel, where you'll need to create fields for the data you want to track, like a "Product Line" drop-down or a text field for "Order ID."

Zendesk lets you use a few field types for this:

• Drop-down & Multi-select: These are your best bet for standardized data with a fixed list of options, like product names, subscription plans, or common problem types.

• Regex: This one is for data that follows a specific pattern. If your order numbers always look like "ORD-12345" or tracking numbers have a set format, you can write a regular expression to sniff them out. It’s a powerful tool for developers, but for the average admin, writing and debugging Regex can be a real headache.

This field-first approach means the system is pretty rigid. Every time a new product launches or you identify a new type of issue, an admin has to manually update the custom field and re-tweak the entity detection settings. It’s a bit of a chore.

Fine-tuning Zendesk entity detection with synonyms and rules

Once your fields are linked, Zendesk gives you a few options to try and dial in the accuracy.

You can add synonyms so that different words point to the same value. For instance, "ID," "Order #," and "Order Number" could all be synonyms for your order number entity. This helps catch some of the ways customers naturally talk.

You also get to set extraction rules that decide when a field gets updated. Do you want to grab the entity from the very first message only, or should it update if a customer mentions it again later? You have some control there.

There's also a misspelling detection feature, but it has some strange quirks. It only works on words longer than five letters, and the customer has to get the first letter right. It’s a nice idea, but it’s a long way from being a truly "intelligent" feature that can handle real-world typos.

The headache of Regex and entity order

Here’s a big gotcha that trips up a lot of Zendesk admins: the order of your entities is critically important.

Zendesk scans for entities in a ticket from the top of your list to the bottom. The very first one it finds that matches the text, it grabs, and then it stops looking. If you’re not careful, this can cause all sorts of mis-categorizations.

For example, say you have a broad entity for "Full Name" at the top of your list. A bit further down, you have a very specific entity that looks for an "Email Address." If a customer writes, "My email is hello@example.com," the "Full Name" rule might grab "hello@example.com" first and wrongly tag it as a name. The more specific email rule never even gets a chance to run.

This forces admins into a frustrating cycle of testing, debugging, and constantly re-ordering their entity list to avoid conflicts. It’s a brittle setup that creates a surprising amount of ongoing maintenance.

Common use cases and key limitations of Zendesk entity detection

While Zendesk’s feature is a decent starting point for automation, its strict, rule-based design shows its age when compared to modern AI that understands context.

What Zendesk entity detection is good for

To be fair, it has its moments. For simple, clearly defined tasks, it can work pretty well.

• Automated Ticket Routing: This is its main purpose. If the entity "Camera Model A" is detected, a trigger can fire off and send the ticket straight to your hardware specialists.

• Setting Ticket Priority: If a ticket mentions the entity "System Outage," you can create a rule to automatically flag it as Urgent.

• Reporting: It helps you generate structured reports on ticket volumes related to specific products or issue types you've defined.

Where the system falls short

The problems start when you need to handle the gray areas of customer support.

• It’s rigid, not intelligent: The system isn’t actually understanding the customer. It’s just playing a game of keyword matching based on a list you gave it. If a customer describes their issue with slightly different words that aren't on your synonym list, the entity is missed entirely. There's no real contextual awareness.

• It creates a lot of admin work: As any seasoned admin will tell you, a huge amount of time gets sunk into writing and debugging Regex, getting the entity order just right, and manually adding every synonym you can think of. It’s a system that needs constant feeding and care, not one that learns on its own.

• Its knowledge is siloed: Entity detection can only see the information you’ve manually typed into Zendesk custom fields. It has no idea what’s in your Confluence knowledge base, internal Google Docs, or what was said in past ticket resolutions.

• The actions are limited: At the end of the day, all this feature does is fill in a ticket field. If you want to do something more complex, like ping a product manager in Slack or look up order details in Shopify, you have to build out a separate, often clunky, web of triggers that can easily break.

A more flexible alternative: eesel AI's contextual understanding

This is where a tool like eesel AI approaches the problem from a completely different angle. Instead of relying on rigid, predefined rules, eesel AI connects to all of your company’s knowledge, past tickets, help articles, Confluence pages, Slack messages, and more, to learn your business context automatically.

It doesn’t just match keywords; it understands what the customer is trying to say. This allows it to categorize tickets with a much higher degree of accuracy, even when people use new or unexpected phrasing.

Better yet, eesel AI does more than just fill out a field. Its self-serve workflow builder lets you create powerful automations without being a developer. You can set up custom actions to look up order info from your database, update a user's account in your CRM, or escalate an issue to a specific engineer in Jira, all from one place.

Feature	Zendesk Entity Detection	eesel AI
Setup	Manual; involves creating custom fields, writing Regex, and ordering entities.	Go live in minutes; one-click integrations that learn on their own.
Knowledge Source	Limited to predefined values you've entered in Zendesk custom fields.	Unified; learns from past tickets, help centers, Confluence, Google Docs, etc.
Flexibility	Rigid and rule-based; easily confused by new or different phrasing.	Context-aware; understands customer intent and nuance using LLMs.
Actions	Mostly just populates a ticket field. Other actions need separate triggers.	Fully customizable; can triage tickets, call APIs, and update external systems.
Testing	A simple text box to test one pattern at a time.	A powerful simulation mode to test on thousands of past tickets before you go live.

Zendesk AI pricing: What to expect

Zendesk’s AI features, including Intelligent Triage and entity detection, aren’t included in every plan. You’ll need to be on one of their pricier tiers to even get access:

• Suite Team: $55 per agent/month (billed annually)

• Suite Professional: $115 per agent/month (billed annually)

• Suite Enterprise: $169 per agent/month (billed annually)

For the most advanced tools, like the AI Copilot for agents, you’ll probably need the Advanced AI add-on, which can be an extra $50 per agent, per month.

But here’s the real kicker: Zendesk is shifting to resolution-based pricing for some AI tools. This means you get charged a fee (often around $1.50 - $2.00) every single time the AI successfully closes a ticket for you. While it sounds reasonable, it makes your costs completely unpredictable. A busy month could lead to a massive bill, making it almost impossible to budget properly.

This is a huge difference from eesel AI's transparent pricing. Our plans are based on a predictable monthly interaction volume, with no surprise per-resolution fees. You can scale your automation without worrying that your bill is going to suddenly balloon.

This video explains how Zendesk Entity Detection works and how it can boost your customer support efficiency.

Ditch the rules and embrace automation that just works

Zendesk entity detection is a fine first step into support automation. But its total reliance on rigid rules, complex Regex, and constant manual upkeep makes it a brittle and time-consuming tool for any team that’s trying to grow or adapt. The system just can’t keep up without a ton of administrative effort, and its knowledge is stuck in a Zendesk-shaped box.

Modern support teams need an AI partner that’s flexible, truly understands context, and can tap into all of your company’s knowledge, not just a tiny piece of it.

That's what eesel AI is all about. By bringing together all your knowledge sources and giving you a simple-yet-powerful workflow engine, eesel AI goes beyond basic pattern matching to deliver automation you can actually rely on. With a setup process that’s ridiculously simple and pricing that’s predictable, you can get started in minutes, not months.

Ready to see what a modern AI support platform can really do? Start your free eesel AI trial today and automate your first workflow in minutes.