Pricing and quotation guide
AI support ticket classification cost
Ticket classification can be inexpensive when your helpdesk already offers AI labelling and the main work is cleaning up labels and examples, or more involved when a custom classifier is built or several dimensions such as topic, product, priority and sentiment are classified. Costs depend on how much label clean-up is needed, how many examples must be checked, whether a built-in feature or custom build is used, how many dimensions are classified and how thoroughly accuracy is tested. Running costs include helpdesk add-on fees or AI usage fees and agent time for overrides and monthly review.
This guide covers cost drivers, options compared, pricing approaches, running costs and comparing quotes. It gives no prices, because they depend on your helpdesk, volume and scope; LetGetGrow sets out the cost for your project in a custom quotation.
Illustrative diagram
What drives classification cost
- Label clean-up
- Merging and defining labels takes agent and provider time.
- Example set
- Checking example tickets per label takes time.
- Built-in or custom
- Helpdesk features are quicker; custom builds take longer.
- Dimensions
- Each extra dimension needs labels, examples and tests.
- Testing
- Per-label accuracy testing reduces risk.
Costs paid to third parties (separate from our fees)
- Helpdesk AI add-on
- AI usage fees for custom builds
- Reporting tools
- Hosting for custom classifiers
Ask whether your helpdesk's own feature would meet the need before paying for a custom build.
Options compared
| Option | What it involves | Relative cost |
|---|---|---|
| Built-in helpdesk feature | Label clean-up, examples, configuration | Lower; add-on fee |
| Built-in plus reporting | As above with dashboards for teams | Moderate |
| Custom classifier | Built for your labels, integrated with helpdesk | Higher; usage and hosting |
How classification is priced
Fixed setup
Suits you when
- Helpdesk feature used
Watch out for
- Label clean-up included
Custom build plus usage
Suits you when
- Built-in insufficient
Watch out for
- Usage estimates
Monthly review service
Suits you when
- Ongoing tuning wanted
Watch out for
- Hours and reports included
Label clean-up
If labels have grown into a long, inconsistent list, cleaning them up is the first and most valuable step. It needs a few hours of experienced agents' time as well as the provider's. It improves reporting even before AI is switched on.
More dimensions
Classifying topic alone is the simplest. Adding product, priority, sentiment or language each needs its own definitions, examples and testing. Start with the dimension that drives most value, usually topic.
Example: using what the helpdesk already offers
Illustrative example — not a client case study
Situation: A retailer's support team considers a custom classifier.
- The provider checks the helpdesk's built-in feature first.
- With labels cleaned up and examples checked, the built-in feature reaches acceptable agreement.
- The custom build is not needed, saving build and hosting costs.
What this shows: The retailer paid mainly for label work, which also improved reports. This example is illustrative.
Running costs
Helpdesk AI add-ons are usually priced per agent or per ticket; custom classifiers incur AI usage and hosting fees. Agent overrides and monthly reviews take time. Estimate all of these at your ticket volume.
Comparing quotes
Check label clean-up, example set, built-in or custom, dimensions, testing per label, rollout approach, reporting, running fees and review support.
Keeping the cost down
Use your helpdesk's built-in feature if it fits, classify topic first, check examples in-house and review overrides as part of team meetings. Start with suggestions only, which needs less testing before launch.
Testing
Comparing AI labels with agent labels on past tickets, label by label, shows where the classifier is reliable. It is quick for a small label set and longer for many labels and dimensions.
Judging the value
Value shows in faster routing, less time spent labelling and reports reliable enough to act on. Track label agreement, time saved and decisions made from reports. Ask agents too whether the labels save them time.
Example set
Checking a set of correctly labelled tickets for each label needs agent time. The more labels, the more examples. A clean example set is what makes classification accurate, so it is worth the effort, and it doubles as training material for new agents.
Reporting
Turning labels into dashboards for support, product and operations teams may be included in your helpdesk or need a reporting tool. Price it if you need it, since reporting is often where the value of consistent labels shows.
Monthly review
Reviewing overrides and new ticket types each month keeps labels meaningful. Whether your team does it or a provider, budget a few hours.
Routing from labels
Using labels to route tickets to teams adds configuration and testing in the helpdesk. It is worth doing once labels are reliable, and usually costs little extra if planned with the classification.
Questions about AI support ticket classification cost
What affects ticket classification cost?
Label clean-up, examples, built-in or custom, dimensions and testing.
Is a built-in feature cheaper?
Usually, though it may carry an add-on fee.
Why pay for label clean-up?
Clear labels improve AI results and reports.
Do extra dimensions add cost?
Each needs definitions, examples and testing.
What does classification cost after setup?
Add-on or usage fees and agent review time.
How do we tell if classification is worth it?
Routing speed, labelling time saved and useful reports.
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