Product Introduction
Meet-Ting is a free AI assistant designed to automate meeting scheduling by interpreting natural-language email conversations and coordinating calendars. It operates directly within email threads by being CC'd, eliminating the need for separate scheduling interfaces or pre-set availability links. The system uses large language models (LLMs) to parse unstructured discussions, detect proposed time options, and resolve scheduling conflicts across participants. Unlike rigid scheduling tools, it adapts to the fluid nature of human-written email exchanges while maintaining calendar accuracy.
The core value lies in reducing administrative overhead by 80% through AI interpretation of real-world email negotiations, while maintaining human-like communication nuances. It eliminates the back-and-forth of manual scheduling by automatically suggesting conflict-free time slots based on live calendar data from all participants. The solution preserves email as the primary communication channel while adding structured automation through LLM-powered analysis of message content and intent.
Main Features
Natural Language Processing Engine analyzes email threads to identify proposed meeting times, participant availability preferences, and contextual requirements (duration, location, video platform) using transformer-based models fine-tuned on scheduling conversations. The system detects both explicit time suggestions ("How about Thursday 2 PM?") and implicit availability cues ("I'm free after the board meeting") across email histories.
Multi-Calendar Synchronization integrates with Google Calendar, Microsoft Outlook, and Apple iCloud to check real-time availability across all meeting participants. The system accounts for different time zones, working hours preferences, and recurring commitments while suggesting optimal time slots. It maintains OAuth 2.0 security protocols without storing raw calendar data.
Automated Invite Management generates calendar invites with correct video conferencing links (Zoom, Teams, Meet) based on organizational defaults or user preferences detected from historical meetings. The AI handles participant RSVPs, rescheduling requests, and calendar updates through direct email interactions, maintaining a complete audit trail in the original thread.
Problems Solved
Eliminates the 7.8 average emails required per meeting setup by automatically resolving scheduling conflicts and formalizing tentative time discussions. Traditional scheduling methods fail when participants propose multiple overlapping options or forget to check calendars before suggesting times, leading to prolonged negotiations that Meet-Ting's AI curtails through instant analysis.
Serves busy professionals (executives, sales teams, recruiters) who manage 15+ weekly meetings through email correspondence. The solution particularly benefits cross-organizational scheduling where participants lack shared calendar visibility, as the AI acts as a neutral mediator with access to all relevant availability data.
Addresses scenarios like coordinating multi-timezone client meetings where participants mention availability in local time references, or resolving complex team scheduling when members propose times across different email responses. The system handles last-minute changes by automatically proposing alternative slots when conflicts arise post-confirmation.
Unique Advantages
Unlike Calendly's requirement for structured availability sharing, Meet-Ting works within existing email workflows without forcing participants to adopt new tools. The AI handles the informality of real communication ("I'm free Tuesday morning, but need to pick up kids at 3") that rigid scheduling systems typically reject, converting natural language into actionable schedule data.
Proprietary LLM training on 2.3 million anonymized scheduling emails enables superior context understanding, including detecting tentative agreements ("Let's aim for Wednesday"), conditional availability ("If the client approves"), and time range preferences ("Anytime next week except Friday afternoon"). The system achieves 94% accuracy in time extraction versus 78% for generic NLP models.
Combines the automation of calendar bots with human communication patterns through features like polite follow-ups ("Just checking if 2 PM still works for everyone") and contextual rescheduling logic that preserves meeting urgency ratings. The closed beta prioritization for Product Hunt users creates viral growth potential while maintaining service quality during scaling.
Frequently Asked Questions (FAQ)
How does Meet-Ting ensure calendar security? Meet-Ting uses read-only OAuth access to calendars with zero data storage, processing all information in transient memory during scheduling operations. The system is SOC 2 Type II compliant and automatically revokes access if users remove integration, with enterprise-grade encryption for all processed email content.
Can it handle complex meetings with 10+ participants? Yes, the algorithm prioritizes key attendees' availability first before optimizing for group consensus, using a tiered priority system that users can configure. For large groups, it implements combinatorial optimization to find the maximum-availability slot while respecting mandatory participant constraints.
What makes Meet-Ting better than Calendly? Unlike Calendly's one-way availability sharing, Meet-Ting mediates multi-party negotiations in existing email threads and adapts to real-time changes. It understands "Maybe" responses and partial availabilities that Calendly treats as hard declines, reducing average scheduling time from 42 hours to 9 minutes according to beta tests.
How does the AI handle time zone differences? The system automatically detects stated time zones ("Let's meet at 3 PM CST") and converts all proposals to each participant's local time for confirmation. For unspecified times, it cross-references the sender's calendar settings and historical meeting patterns to assume probable time zones with 89% accuracy.
Can I use Meet-Ting without revealing my full calendar? Yes, users can set availability windows that limit when the AI proposes meetings, separate from actual calendar content. The system also supports "private blocks" that appear as busy slots without revealing event details, maintaining schedule privacy while enabling efficient booking.
