Timetics

Converts conversational user inputs into structured calendar operations through NLP-based intent recognition and entity extraction. The system parses natural language phrases like 'schedule a meeting with John next Tuesday at 2pm' into discrete calendar events with attendees, times, and metadata, eliminating the need for manual form-filling or calendar UI navigation.

Monitors user calendar state in real-time and automatically identifies scheduling conflicts, double-bookings, and availability gaps when new appointment requests arrive. The system cross-references proposed meeting times against existing calendar entries, timezone differences, and buffer preferences, then suggests alternative slots or blocks conflicting requests before they're confirmed.

Allows users to define their working hours, availability windows, and blackout periods (vacation, blocked time) that constrain when appointments can be scheduled. The system uses these rules to filter available time slots presented to clients, prevent bookings outside working hours, and automatically block time for personal commitments or administrative work.

Enables users to attach notes, custom fields, and metadata to appointments for context and follow-up purposes. The system stores structured and unstructured data associated with each appointment (meeting notes, client preferences, follow-up tasks, custom fields) and makes this information accessible to team members and in post-appointment workflows.

Generates and sends confirmation messages to attendees after scheduling, then triggers reminder notifications at configurable intervals (e.g., 24 hours, 1 hour before meeting). The system uses templated message generation with dynamic variable substitution (meeting time, attendee names, meeting link) and supports multi-channel delivery (email, SMS, in-app notifications) based on user preferences.

Processes payments directly within the scheduling workflow by attaching payment requests to appointments and generating invoices automatically after service completion. The system supports multiple payment methods (credit card, bank transfer, digital wallets) through integrated payment processor APIs (Stripe, PayPal, etc.), calculates amounts based on service duration or fixed rates, and stores payment records linked to calendar events for audit trails.

Maintains real-time synchronization across multiple calendar sources (Google Calendar, Outlook, Apple Calendar, proprietary calendars) by polling calendar APIs at regular intervals and merging events into a unified availability view. The system handles timezone normalization, duplicate detection, and conflict resolution when the same event appears in multiple calendars, presenting a single source of truth for scheduling decisions.

Allows users and clients to modify or cancel appointments through natural language chat commands, with the system automatically detecting conflicts, notifying affected parties, and updating all synchronized calendars. The system parses requests like 'move my 2pm meeting to Thursday' or 'cancel tomorrow's call', validates the change against availability, and triggers notification workflows to inform all attendees of the change.

Provides a conversational chatbot that clients can interact with to book appointments, ask questions about availability, and receive information about services. The chatbot uses NLP to understand booking intent, guides users through the scheduling process with clarifying questions, and integrates with the availability and payment systems to complete bookings without requiring clients to use external booking links or forms.

Allows users to define multiple service offerings with associated pricing, duration, and availability rules through a configuration interface. The system stores service metadata (name, description, duration, rate, buffer time, availability windows) and uses this data to validate booking requests, calculate invoice amounts, and present available time slots to clients based on service-specific constraints.

Maintains a persistent record of each client's past appointments, preferences, and interaction history within the system. The system uses this historical data to personalize the booking experience (e.g., suggesting previously booked service types), provide context in conversations, and enable the chatbot to reference past interactions without requiring users to repeat information.

Automatically detects and normalizes timezones for users and clients across different regions, converting appointment times to local timezone for each party and preventing timezone-related scheduling errors. The system stores appointments in UTC internally and converts to local time for display and notifications, handling daylight saving time transitions and supporting clients across multiple timezones without manual adjustment.