AI & Automation Case Study · Education, Middle East & India

AI Support Automation

For a group of education platforms spanning India and the Middle East, 300+ teacher and parent emails a day used to be read and answered by hand. Today an AI agent processes each one and escalates only the exceptions.

Measurable results

Before and after, in numbers

customer emails processed per day
300+
first response time
~24h → <5 min
of manual support work saved per month
800+ hours
of requests resolved without human touch
70%

Before

Every email was a manual task

Three branded support inboxes received 300+ emails a day — teachers completing paid profiles with real billing behind them, parents booking tutors, students checking lessons. Every message was read, triaged and answered by a person, often in the local language.

First responses took up to 24 hours on busy weeks, the team was buried in repetitive copy-paste replies, and genuinely urgent requests waited in the same queue as routine questions.

After

An AI agent that runs the inbox

The agent works inside the shared Gmail inbox and the platforms' own lookup services — no new tools for the team to adopt.

Step 1

Understands the request

Incoming mail is classified — teacher onboarding, parent inquiry, student question, complaint — and matched to the sender's account via a secure production lookup (no passwords or tokens exposed).

Step 2

Gathers the facts

The agent reads the thread history plus the account record — role, profile completeness, sequence status, activity counts — so every reply relies on verified context, not the email alone.

Step 3

Drafts the response

A ready-to-send reply is drafted in the sender's language (plus English summary) using the correct market brand identity — routine requests are answered directly, the rest prepared for one-click approval.

Step 4

Escalates exceptions

Refunds, legal threats and unhappy VIP customers go straight to a human — with a summary, the history and a suggested reply attached.

What changed for the team

First response time dropped from roughly 24 hours to under 5 minutes, around the clock. About 70% of all requests are now fully resolved by the agent, and the team reviews drafts instead of writing every reply from scratch.

The result: more than 800 hours of manual support work saved every month — and a support team that finally spends its time on the customers who actually need a human.

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