Education & Training

Private Tutoring Institute

Private education — a private tutoring institute/center

Instead of manually cross-referencing subject, teacher availability and student availability in a spreadsheet — the call handler types one sentence in free language, and the system returns the match.

n8n
Google Sheets
AI API (LLM)
Supabase
Railway
WhatsApp Business API
Challenge

The Challenge

The institute assigned students to teachers manually in Google Sheets and Excel files: for every request, someone had to eyeball the subject, the days and hours each teacher and student were available, and account for the constraint that teachers need to know their schedule a week in advance. As the business grew this became a bottleneck — students got stuck on a "waiting to be assigned" list with no orderly tracking, and every assignment consumed long minutes of repetitive work. The owner even tried to build an AI agent himself in an off-the-shelf automation tool, sat on it for hours, "and it always messed up and got it wrong and brought me a student as a teacher." The result: to keep up with the pace you'd have to add headcount — exactly what the institute wanted to avoid. In his words: "Scheduling is a headache."

Solution

The Solution

The client received a smart scheduling engine built on n8n and AI that speaks the team's language, not the other way around. Instead of filling in rigid fields, the call handler or manager writes a free sentence — "the kid wants math, free Thursday 2–4" — and the system breaks it down into a structured scheduling request, reads the teacher and timetable data from Google Sheets, and cross-references subject, availability and business rules defined together to return the right matches. The core of the success is something the client requested explicitly: control. The system works in a recommend-and-approve mode — it thinks, recommends and even updates the table, but the final decision and the communication with the teacher stay in the manager's hands, so the teachers' weekly flexibility is not compromised. The "waiting to be assigned" list updates automatically with a clear status for each student (missing information / ready to search / assigned), so no case falls through the cracks. Later, after the foundation proved itself, the system was extended into a dedicated platform (Supabase + Railway) with a real-time digital scheduling board for teachers, an AI agent that proposes scheduling solutions including shift moves for stuck students, and a voice assistant that performs actions in the dashboard on a spoken or written request.

Our Approach

A smart scheduling engine that reads from Google Sheets and cross-references subject, teacher availability and student availability according to defined business rules
A free-language input interface that converts free text into a structured scheduling request, including handling cases where information is missing
Automatic synchronization of the 'waiting to be assigned' table with a status for each student: missing information / ready to search / assigned
A semi-automatic recommend-and-manager-approve mode — the system recommends and updates the data, the decision and communication with the teacher stay with the person in charge
Testing against real-world scenarios to prevent the failure that sank the previous automation-tool attempt (confusing a student with a teacher)
Extension into a dedicated platform (Supabase + Railway): a real-time digital scheduling board for teachers, an AI agent for scheduling proposals with shift moves, and a voice assistant to perform actions in the dashboard
A drag-and-drop make-up lesson on the teacher's board, with an automatic WhatsApp message to the parent

Technologies Used

n8nGoogle SheetsAI API (LLM)SupabaseRailwayWhatsApp Business APISpeech recognition

The service behind this project

Automatic Appointment Scheduling
Results

The Results

The institute moved from manual scheduling that "consumed long minutes of cross-referencing" to a process where the call handler writes one sentence and the system returns the match — without losing the human control and the accuracy that were missing in the previous attempt with an off-the-shelf automation tool. The "waiting to be assigned" list went from a sticking point to an orderly process with a clear status for each student. The strongest sign of success: the client didn't settle for the first solution — they went on to order additional upgrades and expanded the project into a full platform (a digital teacher board, an AI agent for scheduling proposals, a voice assistant). This enabled the institute to grow without adding headcount — exactly the goal defined at the outset. (There are no documented quantitative metrics — the results are stated qualitatively.)
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