Metro rail corporations are the lifeline of modern urban transportation—moving millions of people daily with precision and timeliness. Yet, behind this logistical marvel lies an operational quagmire: ticket management and customer issue resolution. Passenger complaints, lost-and-found requests, service delays, infrastructure faults, and safety concerns arrive across fragmented channels—email, social media, mobile apps, kiosks, call centers—overwhelming teams with high volumes, poor categorization, and manual follow-ups.
What further exacerbates this situation is the linguistic diversity of passengers, the inconsistency in complaint formats, and the lack of real-time context needed to resolve tickets efficiently.
Legacy CRM and ITSM tools fall short—offering generic workflows, rigid forms, and zero intelligence. Metro teams remain buried under ticket backlogs, SLA breaches, and irate passengers.
Swiftex Pulse, a low-code/no-code business process automation platform, combined with Agentic AI, introduces a paradigm shift in ticket management. It doesn’t just digitize workflows—it infuses them with adaptive intelligence.
Together, they deliver:
Problem: Complaints come via WhatsApp, emails, apps, kiosks, and social media. The input is free-form, unstructured, and often includes documents or images.
Solution: Swiftex Pulse captures tickets from all these channels. Agentic AI reads not just the text, but also scanned documents and image attachments using OCR, adding extracted information (like PNR numbers, date stamps, handwritten remarks) directly into the ticket context. This ensures no detail is missed.
Problem: Complaints vary in tone, clarity, and language—often written in local dialects, mixing Hindi, English, or regional tongues, making it hard to categorize or prioritize.
Solution: LLMs trained on multilingual corpora interpret intent, sentiment, and problem category across languages. Whether the complaint is in Tamil, Marathi, or Hinglish, AI understands the root issue, urgency, and action needed.
Problem: Ticket routing relies on human intervention or basic keywords, leading to misassignment and wasted cycles.
Solution: Agentic AI uses intent-scenario matching and context-sensitive auto-assignment to direct the ticket to the correct team or zone—based on complaint type, station code, time of day, and resource availability.
Problem: Teams often miss SLA deadlines due to lack of alerting, visibility, and handover delays.
Solution: Swiftex Pulse’s SLA monitoring system alerts users as deadlines near. Escalation rules automatically kick in if resolution is delayed. Each step is logged, visible, and governed.
Problem: Many tickets could be resolved instantly if the customer had access to correct data or if the agent had tools to assist.
Solution: Swiftex Pulse integrates LLM agents with tools, allowing AI to fetch live operational data (train status, refund policy, baggage handling SOP) and resolve tickets autonomously. The response is delivered on the same channel the ticket originated from.
Problem: Standard messages don’t address emotional tone or urgency of different passengers.
Solution: Agentic AI uses tone-adjusted response generation, ensuring that a safety-related concern receives empathetic attention, while routine queries are answered efficiently.
Problem: Full automation can feel cold, especially in sensitive cases.
Solution: Agentic AI powers humanoid voice bots that call customers, explain issues or actions, and handoff to live agents in real-time if needed. This keeps the human connection intact.
Swiftex Pulse and Agentic AI aren’t limited to complaints. They can:
Swiftex Pulse with Agentic AI doesn’t just improve ticketing—it transforms the entire service delivery model. From intelligent complaint triaging to proactive customer care, from multilingual understanding to voice-enabled empathy, this solution offers metro teams a scalable, smart, and humane approach to operations.
It’s time for metro corporations to move beyond legacy CRM and into an AI-native future of ticket and operations management.
Start with a 30-day pilot. Measure impact. Scale city-wide.