API-first scheduling engine built on Next.js and Supabase with Redis-backed tentative holds for concurrency control, interval tree data structures for O(log n) conflict detection, and constraint propagation algorithms for multi-resource slot calculation. All timestamps stored UTC with IANA timezone identifiers; recurring appointments resolved at query time for correct DST handling. Multi-tenant isolation via PostgreSQL Row Level Security.
How do you prevent double-bookings under high concurrent load?
We use a three-layer approach: Redis-based tentative holds with TTL when users enter the booking flow, PostgreSQL advisory locks for atomic confirmation, and database-level constraints as a final safety net. This eliminates race conditions even at thousands of concurrent booking attempts. The tentative hold pattern cuts database contention by 90%+ compared to pessimistic locking.
How does multi-timezone scheduling handle DST transitions?
All timestamps are stored in UTC with IANA timezone identifiers. Recurring appointments store the recurrence rule in the original timezone and generate instances at query time using the Temporal API. This means a weekly 9 AM appointment stays at 9 AM local time across DST transitions — the UTC representation shifts automatically. We never use fixed offsets.
Can this integrate with our existing ERP and CRM systems?
Yes. The platform is API-first, so every operation is available via REST endpoints and webhook events. We've integrated with Salesforce, HubSpot, custom ERPs, and legacy systems via middleware. Calendar sync with Google Workspace and Microsoft 365 is bi-directional and near-real-time. Custom integrations are scoped during discovery.
What kind of throughput can the scheduling engine handle?
Our architecture is load-tested at 10,000+ concurrent booking attempts with sub-second availability calculation. Redis caching for hot availability data, interval trees for conflict detection, and Vercel's auto-scaling serverless functions mean the system scales horizontally. For most enterprise clients handling 5,000-50,000 daily bookings, that's well within comfortable operating range.
How long does it take to build and launch an enterprise scheduling platform?
Typical enterprise scheduling platforms run 12-20 weeks from kickoff to production. Core booking functionality ships by week 6, with integrations, multi-timezone hardening, and load testing filling weeks 7-14. Complex multi-location rollouts or legacy migrations can push that to 20 weeks. We deliver incrementally so you can validate each milestone.
Why not use Calendly, Acuity, or another SaaS scheduling tool?
SaaS tools work fine for simple use cases. They break when you need multi-resource constraint satisfaction, custom business rules per service type, complex conflict resolution workflows, or integration with proprietary systems. You also end up with vendor lock-in on your most critical operational data. Custom platforms cost more upfront but eliminate the ongoing pain of forcing enterprise logic into consumer software.
Is the platform HIPAA or GDPR compliant?
We build with compliance in mind from day one. Supabase provides Row Level Security for data isolation, all PII is encrypted at rest and in transit, and audit logs capture every data access event. For HIPAA, we deploy on HIPAA-eligible infrastructure with BAAs in place. GDPR features include consent management, data export, and right-to-deletion workflows built into the admin dashboard.
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