Da1TimeTable — AI-Powered Academic Scheduling Platform

Da1TimeTable is a full-scale SaaS platform built to automate one of the most complex operational challenges in education: creating and managing conflict-free academic timetables.

I worked on the product as a full-stack engineer, contributing across the application architecture, user-facing workflows, backend functionality, AI-powered scheduling experience, payment integrations, and production SaaS infrastructure. The objective was not simply to digitize a traditional timetable—it was to create an intelligent scheduling system capable of processing institutional constraints and producing practical schedules in minutes instead of days or weeks.

At the core of the platform is an AI-powered timetable generation engine. Schools can provide their teachers, subjects, classes, rooms, availability rules, academic requirements, and scheduling constraints. The system processes these inputs together to generate an optimized timetable while minimizing teacher clashes, room conflicts, scheduling overlaps, and inefficient resource allocation.

The workflow was designed to remain simple for school administrators despite the complexity happening behind the scenes: institutions provide their academic data, configure their scheduling requirements, run the AI optimization process, review the generated timetable, make adjustments when necessary, and then publish or export the final schedule.

Key Product & Engineering Features

AI Timetable Generation — Automatically generates complete academic schedules from teachers, classes, subjects, rooms, availability, and institutional constraints.

Intelligent Conflict Resolution — Detects and prevents teacher double-booking, room conflicts, overlapping classes, and incompatible scheduling combinations.

Teacher Workload Balancing — Scheduling logic helps distribute teaching periods more effectively instead of producing heavily concentrated or impractical workloads.

Real-Time Schedule Adjustments — Administrators can modify generated schedules while the system continues validating changes against scheduling constraints.

Drag-and-Drop Timetable Management — A visual scheduling experience makes manual refinement significantly easier after AI generation.

Guided & Quick Scheduling Workflows — Different generation flows support both administrators configuring complex timetables and experienced users who need faster scheduling.

Classes, Teachers, Subjects & Rooms Management — Centralized academic data management provides the scheduling engine with structured institutional information.

Availability & Academic Constraints — Institutions can model teacher availability, room requirements, academic rules, preferred periods, and other scheduling limitations.

Instant Conflict Alerts — Scheduling issues can be identified during timetable creation and adjustment instead of being discovered after publication.

Responsive Administrative Dashboard — School administrators can monitor schedules, resources, academic information, and timetable status from one unified interface.

Export & Distribution Workflows — Generated timetables can be prepared for distribution to administrators, teachers, and students without rebuilding schedules manually.

Cloud-Based SaaS Architecture — The platform is accessible through the browser without requiring schools to maintain complicated local installations.

Stripe Integration — Stripe was integrated into the SaaS payment flow to support secure online plan and subscription-related transactions.

Authorize.Net Integration — Authorize.Net was also incorporated into the payment architecture, providing an additional payment-processing workflow for the platform.

Subscription & Plan Management — Commercial SaaS functionality connects account access, payment processing, and product plans to the wider platform experience.

Role-Based Product Experience — Administrative and institutional workflows are structured around the different responsibilities involved in managing academic scheduling.

Reporting & Operational Visibility — Scheduling and institutional data can be surfaced through dashboards and reports, helping administrators understand the state of their academic operations.

Regional & Institutional Flexibility — The product was designed for different academic structures and scheduling requirements rather than forcing every institution into a single rigid scheduling model.

The engineering challenge was primarily about turning a highly constrained optimization problem into a usable SaaS product. Scheduling becomes increasingly difficult as the number of teachers, classes, rooms, subjects, periods, and institutional rules grows. The user experience therefore had to keep the complexity away from administrators while the underlying system handled validation, optimization, data relationships, conflict detection, and schedule generation.

Beyond the scheduling engine, the project required the surrounding infrastructure expected from a production SaaS application: account and access flows, payment processing, plan management, responsive interfaces, administrative tooling, backend APIs, structured data management, reporting, and production-ready workflows.

Product Scale & Impact

Da1TimeTable currently reports 400+ institutions, 200K+ students scheduled, a 98% success rate, availability across 5+ countries, and 100+ administrative hours saved per semester. The platform positions its AI scheduling workflow as approximately 10× faster than traditional manual timetable creation.

The result is a complete education-technology product that combines AI optimization, full-stack SaaS engineering, payment infrastructure, complex academic data modeling, and a user-friendly scheduling experience—allowing institutions to move from manual spreadsheets and scheduling conflicts to an intelligent, scalable timetable-management workflow.

My contribution: Full-stack engineering • SaaS architecture • AI scheduling workflows • Backend/API development • Dashboard & administrative UX • Scheduling logic integration • Stripe integration • Authorize.Net integration • Subscription/payment flows • Data-driven product features • Production implementation