All case studies
EdTech Company

RAG-Based Support Chatbot for an EdTech Company

The team could answer every question a student asked; they just could not get to them fast enough. A WhatsApp agent now replies the moment a query arrives.

Sector
Education technology
Channel
WhatsApp
Engagement
Support automation
The challenge

What was in the way

An EdTech company was struggling to keep up with the sheer number of support queries coming from students: schedules, platform problems, general course questions. The team knew the answers; there were simply more questions arriving than hours in the day, and students waited.

  • 01
    Query volume outpacing the team

    More questions arriving each day than the support team had hours to work through.

  • 02
    Response time, not answer quality

    The team could answer everything a student asked. The problem was how long students waited in the queue before anyone got to them.

  • 03
    Routine questions absorbing the day

    Schedule and platform queries repeated constantly, consuming the time needed for the questions that genuinely warranted a person.

  • 04
    Student-specific answers

    Many questions could not be answered at all without first knowing which student was asking.

Our solution

What we built

We built a WhatsApp-based AI chatbot that replies the instant a message arrives. It identifies the student from the number they are messaging from, answers from the company's own question bank, and escalates anything it is not confident about to a support agent.

01

Student Lookup by Phone Number

The chatbot resolves the student from the number they message from, so schedule and account questions are answered against their own record.

02

Grounded on the Question Bank

Retrieval runs over the company's existing question bank, so general answers come from approved content rather than the model's own recall.

03

Confidence-Based Escalation

Where the chatbot is not confident, the conversation is handed to a support agent rather than guessed at, leaving the team the queries that genuinely need a person, and the time to answer them.

04

Automated Reminders

Schedule and deadline reminders delivered on the channel students already use, rather than a portal they have to remember to check.

05

Captured Knowledge Gaps

Every question the chatbot struggled with is logged for review and addition to the question bank, so coverage improves with use.

Impact & results

What changed

Immediate response

Students get an answer as soon as they ask, instead of waiting in a queue.

Volume absorbed

Routine schedule, platform and course queries resolved without occupying the team.

Escalation, not guesswork

Low-confidence questions go to an agent, and each one is logged for the question bank.

Next case study

Metadata-Driven Automated Lakehouse Solution

Read it