Organisation Profile
Business Challenges Before FawksAI
- 70% of inbound calls unanswered during peak admission season — 28 counselors, 1,200 daily calls
- Repetitive queries (eligibility, fees, dates) consuming 70% of counselor bandwidth
- Application status calls requiring manual CRM lookup — 4–7 minutes each, 200+ daily
- 18% of calls were tech support (portal login, password reset) — not counselor expertise
- 22,000 incomplete applications per year with zero automated follow-up system
The Challenge: 1,200 Calls Per Day, 28 Counselors
Vidyasetu Open University — a state-recognised institution with 1.5 lakh annual enrollments across 40+ UG, PG and certificate programmes — faced a crisis every admission season. Their 28 counselors were handling fewer than 30% of inbound student calls during peak months.
The remaining 70% — students asking about application deadlines, eligibility criteria, course details, fee structures, exam schedules, and distance learning portal access — went unanswered. Prospective students called competitors instead. Conversion rates were collapsing.
Root Cause Analysis: Why Calls Were Being Lost
A 3-week audit of call logs revealed four core failure modes:
- 70% of queries were repetitive: Eligibility criteria, last date to apply, fee structure, exam dates — the same 12 questions answered 800 times daily by human counselors
- Application status calls created double work: Students calling 3–4 times to check document verification status. Each call took 4–7 minutes of a counselor's time
- Tech support overwhelmed the helpline: 18% of all calls were students unable to log into the distance learning portal — password resets, browser issues, course access problems
- 22,000 incomplete applications per year: Students who started the application process but didn't complete it received zero follow-up, representing an enormous conversion opportunity
The FawksAI Deployment: Full Technical Walkthrough
FawksAI was deployed across all inbound and outbound channels within 18 days of contract signing. The implementation involved three components:
Component 1 — Inbound AI Voice Agent
A multilingual AI agent (Hindi, English, Marathi) was trained on the university's entire knowledge base: all programme details, fee structures, eligibility matrices, exam calendars, and admission procedures. The agent was connected to the university's student management system via REST API, enabling real-time application status lookups by student ID or mobile number.
Component 2 — Outbound Incomplete Application Recovery
A separate outbound campaign was configured to trigger automatically at D-3 (3 days before the application deadline) for any student who had started but not completed their application. The AI made a personalised call referencing the student's name and the specific programme they were applying for, offered to answer any remaining questions, and provided a direct link to complete the form.
Student Inquiry Automation Workflow — End to End
Every interaction is logged with timestamp, intent, resolution status and satisfaction flag. Counselors receive a real-time dashboard showing unresolved escalations with full transcript context.
Component 3 — Post-Call Automation Sequences
Every inbound inquiry automatically triggered a follow-up sequence based on the detected intent:
Measured Results After 90 Days
The impact was measurable within the first admission cycle post-deployment:
During our peak admission month, students were on hold for 40 minutes. Now the AI handles everything instantly in Hindi and English — course details, fees, eligibility, even application status lookups. Our counselors only speak to students who are genuinely ready to enroll. Conversion went up 4× and our team finally has time to do meaningful work.
Why AI Voice Agents Are the Future of EdTech Student Support
India's higher education sector is experiencing unprecedented demand. With over 4 crore students enrolled in distance and open learning programmes nationally, the volume of support queries scales faster than any institution can hire counselors. AI voice agents solve this permanently — they scale to zero marginal cost per additional call.
The key differentiator for FawksAI in education is native language support across 40+ languages including Hindi, Marathi, Telugu, Tamil, Bengali, and regional dialects — allowing universities to serve students from Tier-2 and Tier-3 cities with the same quality of support as urban students.
Implementation Timeline
- Day 1–5: Knowledge base ingestion — all programme PDFs, FAQs, fee structures loaded and indexed
- Day 6–10: API integration with student management system for live status lookups
- Day 11–15: Voice testing across Hindi, English and Marathi dialects; edge case training
- Day 16–18: Soft launch with 20% call traffic; full rollout on Day 18
- Day 30: First performance review — all KPIs tracking above target