Send every support ticket to the right agent the moment it arrives, so customers wait less and your team stops sorting queues by hand. Ticket Classification and Routing
NLP intent detection, priority scoring, and skill-based routing across voice and chat channels.
Automated ticket classification and routing is a customer support system from KriraAI that reads each incoming ticket, identifies its intent and priority, tags it, and assigns it to the right team or agent without manual triage. It works across voice and chat channels for customer support and experience teams.
The system uses natural language processing to detect what a customer is asking for, scores urgency from the wording and sentiment, then applies a rules engine and skill-based routing to place the ticket with the agent best equipped to resolve it. This removes the manual sorting step where tickets sit in a general queue, get mislabelled, or bounce between teams before anyone starts work. In its 2026 State of AI in the Enterprise report, based on a survey of 3,235 business and IT leaders, Deloitte found that leaders expect agentic AI to have its highest impact in customer support, which is the category this workflow sits inside.
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The classifier reads chat messages directly and transcribes voice calls with automatic speech recognition before analysing them, so a phone query and a WhatsApp message pass through the same intent model. It recognises requests written in Hindi, English, and major Indian regional languages, which matters when your inbound mix spans multiple states. Detected intent, not a keyword match, decides where the ticket goes.
Each ticket is scored for urgency using sentiment analysis and signals such as repeat contact, refund or outage language, and account tier. High-impact issues surface at the top of the queue instead of waiting behind routine questions in a first-in-first-out list. Priority is tied to your service level agreement rules, so a breach risk is flagged before it becomes one.
Tickets are matched to an agent by required skill, language, and live open-case count, rather than dropped into a shared pool. When no specialist is free, the system uses a defined fallback such as round-robin among qualified agents so nothing stalls. This keeps one queue from overloading while another sits idle.
Every ticket receives multi-label tags (for example issue type, product area, and priority) drawn from a taxonomy you define, so the same problem is labelled the same way each time. Consistent tags feed clean reporting, which is where manual tagging usually fails and leaves a large share of tickets marked "other." Accurate categories make volume trends and root causes visible to support leaders.
The workflow connects through REST APIs, webhooks, and connectors to helpdesk platforms such as Zendesk, Freshdesk, Salesforce Service Cloud, and Intercom, plus your CRM for customer history. Classification and routing run inside the tools your agents already use, so there is no separate console to learn. Channel intake covers chat widgets, the WhatsApp Business API, email, and voice.
When an agent reclassifies or reroutes a ticket, that correction is logged and used to retrain the model, so accuracy improves against your actual ticket patterns. Low-confidence tickets are sent to a human triage step instead of being force-fitted into a wrong category. This exception handling keeps edge cases with a person while the routine volume runs automatically.
Access to ticket data uses role-based access control, with encryption in transit and at rest and a timestamped audit trail of every classification and routing decision. The design aligns to SOC 2 and ISO 27001 practices, ISO 42001 for AI management, and India's DPDP Act 2023, with data residency options for India-based operations. Personally identifiable information can be masked before it reaches the model where your policy requires it.
A ticket enters from chat, WhatsApp, email, or a voice call. Voice is transcribed with automatic speech recognition, and the system extracts the message text, customer ID, and channel metadata into one structured record.
The NLP model classifies intent, detects the language, and scores sentiment and urgency, while pulling the customer's recent history from your CRM for context.
The ticket receives multi-label tags from your taxonomy, and a priority is assigned against your SLA rules so time-sensitive issues move ahead.
The ticket is matched to the team or agent by skill, language, and current workload, and any low-confidence case is escalated to human triage rather than guessed.
Agent corrections are logged and fed back into model training, and routing accuracy plus category volumes are reported for review.
Want to see this mapped against your current queue setup before committing to anything?
See Your Ticket Flow MappedA [SIZE] [INDUSTRY] company in [REGION], name withheld under NDA, ran inbound support across voice and chat with manual triage. Agents spent the start of each shift reading, tagging, and reassigning tickets, and misrouted cases were bouncing between teams before resolution began.
KriraAI deployed automated ticket classification and routing connected to the client's existing helpdesk and CRM. The system classified intent across both channels, applied the client's tag taxonomy, scored urgency against SLA rules, and routed each ticket to a skilled, available agent, with a human-in-the-loop step for low-confidence cases.
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Learn moreAutomated ticket classification and routing is a system that reads each incoming support ticket, identifies its intent and priority, tags it against a defined taxonomy, and assigns it to the right team or agent without manual sorting. It replaces the triage step where a person reads and reassigns every ticket by hand. KriraAI builds this to run across voice and chat channels.
The AI decides a ticket's category using natural language processing that interprets the meaning of the message, not just matching keywords. It also scores sentiment and urgency, checks the customer's history from your CRM, and applies multi-label tags such as issue type, product, and priority. Tickets the model is unsure about are sent to a human triage step rather than guessed.
It works for voice support as well as chat. Voice calls are transcribed with automatic speech recognition, then passed through the same intent, sentiment, and routing logic used for chat and messaging. This means a phone query and a chat message are classified and routed on one consistent set of rules.
Yes, the workflow integrates with existing helpdesk platforms through REST APIs, webhooks, and connectors for tools such as Zendesk, Freshdesk, Salesforce Service Cloud, and Intercom, plus your CRM. Classification and routing happen inside the systems your agents already use, so there is no separate interface to adopt. KriraAI configures the connection to your current ticket fields and taxonomy.
The classifier recognises Hindi, English, and major Indian regional languages, which suits support teams handling a mixed-language inbound flow. On privacy, the system is designed to align with India's DPDP Act 2023, using role-based access control, encryption in transit and at rest, an audit trail, and data residency options, with PII masking available before data reaches the model. It is also built to SOC 2 and ISO 27001 practices for organisations with global customers.
No, automated ticket classification and routing does not replace human agents; it removes the manual triage step so agents spend their time resolving issues instead of sorting queues. Tickets the model cannot confidently classify are escalated to a person, and agent corrections train the model to improve over time. Humans stay in control of resolution and of the edge cases that need judgement.
