Auto-tagging / categorization
We set up auto-tagging and categorization of requests: automatic labeling of tickets by topic, type and priority (by rules and/or AI), to speed up routing and get analytics on 'what customers write about'. Honestly upfront: auto-labeling speeds up sorting but is NOT perfect (it misclassifies, needs review and correction); rules/AI require good setup and maintenance (categories change); it helps routing and analytics but does NOT answer tickets or solve problems; and wrong tags distort analytics. We make useful labeling with honest quality control, not a 'magic autopilot'.
Auto-tagging / categorization — overview
Auto-tagging and categorization is the automatic assignment of labels to requests: topic (payment, delivery, bug, feature question), type, urgency/priority, sometimes sentiment. It works on rules (keywords), on AI/ML classification or their combination. The goal is to speed up routing to the right people and get analytics on the request structure. Honestly about 'not perfect', this is key: auto-labeling makes mistakes. Rules do not catch all wordings, AI classification confuses similar categories and does not understand context as a human does. So review and the ability to correct are needed — 'tag it and forget' does not work, especially at the start and in ambiguous cases. Promising '100% accurate automatic categorization' would be dishonest. Honestly about setup and maintenance: labeling quality depends on how well categories are defined and rules/the model are set up. Categories change over time (new products, new problem types), and the system must be maintained and adjusted. It is a living process, not a one-off setup. Honestly about 'helps but does not solve': auto-tagging speeds up SORTING and gives analytics but does not answer tickets or solve customer problems — it is an organizing layer, not support itself. A 'bug' tag does not fix the bug or answer the customer. Honestly about analytics: labeling gives a valuable picture of 'what people write about most', but if tags are wrong or inconsistent, analytics will mislead (garbage in — garbage in reports). Analytics quality = labeling quality. Honestly about the effect: with good setup and control it speeds up routing and gives useful analytics, but it is a tool, not a guarantee of support speed/quality. Honestly about access: a request flow, helpdesk/tickets (1006/1007), a setup resource are needed. An important boundary: this is labeling/routing; AI answer suggestions — 1021; AI thread summary — 1022; helpdesk — 1006. Picture this: instead of 'manual sorting and an unclear request structure' — fast auto-labeling with quality control and honest analytics. The base price starts from 35,000 ₽ (depends on volume and method).
Problems we solve
- Requests are sorted manually — slow and uneven.
- The request structure is unclear: what people write about most.
- Tickets reach the wrong people due to poor categorization.
- Past tags were wrong and analytics misled.
What's included in the Auto-tagging / categorization service
- Auto-tagging/categorization (rules and/or AI) for your topics
- Routing by tags to the right people
- Review and correction (against blind trust in auto-labeling)
- Request-structure analytics (with honesty about tag quality)
- Honest boundaries (not perfect; needs maintenance; helps not solves; wrong tags = wrong analytics)
- A link with helpdesk/tickets (1006/1007), AI suggestions (1021)
- A category-maintenance plan (a living process)
- Handover and review with you
What you get
- Fast auto-labeling and routing of requests
- Clear analytics of the request structure
- Tag quality control (not blind trust)
- Honest boundaries (not perfect; helps not solves; needs maintenance)
How the work goes: steps
- We define categories/tags for your real requests
- We set up rules/AI classification and routing
- We build in review, correction and maintenance, set boundaries with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Will auto-categorization be 100% accurate?
No, honestly: auto-labeling makes mistakes. Rules do not catch all wordings, AI classification confuses similar categories and does not understand context as a human does. Review and the ability to correct are needed — 'tag it and forget' does not work, especially at the start and in ambiguous cases. Promising '100% accurate automatic categorization' would be dishonest. We make useful labeling with quality control, not a magic autopilot.
Will auto-tagging speed up answers and solve customer problems?
Speed up routing — yes, solve problems — no, honestly. Auto-tagging speeds up SORTING and gives analytics but does not answer tickets or solve customer problems — it is an organizing layer, not support itself. A 'bug' tag does not fix the bug or answer the customer. We honestly separate: labeling helps route and measure, but people (or AI answers — 1021) answer and solve.
Set up tags once — and that's it?
No, honestly: labeling quality depends on how categories are defined and rules/the model are set up, and categories change over time (new products, new problem types). The system must be maintained and adjusted — a living process. Plus wrong or inconsistent tags distort analytics (garbage in — garbage in reports). We build in category maintenance and quality control rather than a one-off setup.
About the provider
The «Auto-tagging / categorization» service is provided by PDV Expert — a team specialising in «Customer retention». We work under contract and deliver a written report with recommendations.