Student Services & Welfare Analytics

Hostel occupancy analytics break when online allocations differ from who actually stays, and maintenance tickets sit in a separate helpdesk. Wardens need hostel occupancy and maintenance tracking in one place to balance gender blocks, NRI wings, and repair SLAs before inspections or parent tours highlight gaps.

FireAI joins bed inventory, check-in and exit dates, fee tags where relevant, and maintenance work orders. Hostel occupancy analytics show utilization by block, floor, and term, with vacancy drivers (no-shows, early exits) and repeat repair hot spots. Leaders align hostel occupancy analytics to sanctioned capacity in accreditation and finance conversations without a separate audit.

How FireAI solves the problem: It reconciles your rule for occupied nights versus booked beds, flags stale holds, and links maintenance TAT to rooms or common areas you tag so follow-ups stay traceable.

What FireAI tracks:

• Bed-night occupancy and vacancy % by block and category
• Maintenance open, overdue, and repeat-issue counts by building
• Turnaround between checkout and next assignment where turnover matters
• Seasonal demand versus capacity for next intake planning

What you can ask FireAI:

• "Which blocks show the highest vacancy after mid-term when theory-only students leave?"
• "What is our maintenance backlog in girls hostel Block C this month?"

Transport route analytics education needs cost per student and filled seats, not only vendor invoices. When routes are inherited from a prior tender, nobody measures dead kilometers, late starts, or shift overlap with class timings.

FireAI ingests trip logs, student pass assignments, and fee or subvention tags so transport route analytics education shows ridership by route, time band, and stop. You see cost per student trip, under-used corridors, and crowding on others so you rebalance or merge without guessing.

How FireAI solves the problem: It standardizes daily trip completion, student-day attribution rules, and fuel or hire cost allocation you approve, then refreshes as routes or batches change each term.

What FireAI tracks:

• Passengers per trip and per kilometer where odometer data exists
• Cost per student-day or per seat-kilometer for finance review
• On-time start % and late arrival flags tied to class bell times
• Route change simulation notes when you model vendor scenarios

What you can ask FireAI:

• "Which three routes are below 55% average seat fill in the morning shift?"
• "What is our monthly transport cost per day-scholar this year versus last?"

Counseling session analytics require privacy, yet institutions still need volume, wait time, and broad outcome patterns to staff wellness centers. Spreadsheets with names cannot feed leadership safely.

FireAI works with de-identified or role-based feeds you define: session counts, session types, optional outcome codes, and wait days. Counseling session analytics show demand by program, seasonality (exam weeks), and improvement in follow-up rates without exposing individual journals.

How FireAI solves the problem: It enforces the consent and retention policy you publish, stores counselor workload for HR planning, and tags campaigns when you run awareness weeks.

What FireAI tracks:

• Sessions per 100 students by school or program band
• Median days from request to first session where booking data exists
• Outcome or closure codes in aggregate, not raw notes
• Referral to external care handoffs as a process metric

What you can ask FireAI:

• "Did first-session wait days improve after we added two contract counselors?"
• "What share of UG1 sessions are tagged academic stress this term?"

Student grievance tracking fails when email, physical registers, and portal tickets use different ID schemes. Parents and statutory bodies expect student grievance resolution TAT by category, but teams only know anecdotal backlogs.

FireAI normalizes case records, owner assignment, and status changes into student grievance tracking views. student grievance resolution TAT by category, severity, and campus helps you pre-empt reputational issues and staff fairly.

How FireAI solves the problem: It timestamps handoffs, nudges on SLA breach risk you define, and preserves audit history for anti-ragging, discrimination, and safety-related paths where policy demands it.

What FireAI tracks:

• Median and P90 TAT (days) by grievance type
• Reopen rate and escalation count
• Owner workload and age of oldest open case by queue
• Seasonal intake spikes versus staffing plans

What you can ask FireAI:

• "What is median TAT for hostel-related grievances this quarter?"
• "Which category has the longest P90 resolution time?"