Beyond Video Calls: Async-First Collaboration
The remote work revolution taught us that endless video calls are not the answer. The most effective distributed teams in 2026 operate async-first — using written communication, recorded updates, and persistent documents as the primary collaboration medium. GreatChat supports this model with threaded discussions, AI-generated meet summaries, and a real-time collaborative editor that serves as the team's shared workspace. When meets are necessary, they're focused and decision-oriented rather than status-update marathons.
AI Meeting Assistants That Deliver Value
AI meet assistants have matured significantly. Modern tools don't just transcribe — they identify decisions, assign action items, detect blockers, and even suggest agenda items for the next meet based on unresolved topics. GreatChat's Meet assistant integrates with the Mail calendar to prepare briefing documents before meets and distribute comprehensive summaries after. The AI cross-references meet discussions with project files and previous conversations to provide complete context — ensuring no decision gets lost between meets.
Collaborative Editing as the Central Workspace
Documents have become the central collaboration surface for remote teams. GreatChat's Editor experience — built on TipTap and Yjs — provides a real-time collaborative canvas where teams write specs, plan sprints, draft proposals, and document decisions together. Unlike traditional word processors, this editor supports structured blocks (tables, code, diagrams), inline AI assistance, and comment threads that tie directly to project context. The result is a living document culture where information stays current and discoverable.
Building a Persistent Team Knowledge Base
Remote teams generate enormous amounts of valuable information — decisions, research, designs, and code — that often gets buried in chat histories and closed tabs. GreatChat's Projects feature allows teams to organize this information with attachments (up to 20 per project), reference links, and purpose documentation. Combined with search across that internal knowledge base, teams can ask natural language questions and get answers grounded in their own materials — not generic AI responses.