Antispace

Consolidates notifications and messages from email, Slack, GitHub, and calendar into a single AI-indexed feed using a multi-source connector architecture. The system normalizes heterogeneous data formats (IMAP for email, Slack API webhooks, GitHub event streams, CalDAV for calendar) into a unified message schema, then applies semantic ranking to surface high-priority items across all platforms in a single view. This eliminates context-switching by presenting a chronologically and relevance-ordered feed rather than requiring users to check each platform separately.

Enables users to compose emails through natural language prompts rather than traditional text editing, leveraging an LLM to interpret intent and generate contextually appropriate email bodies. The system accepts conversational input (e.g., 'remind John about the deadline next week'), retrieves relevant context from the unified inbox (prior email threads, calendar events, GitHub discussions), and generates a draft email with appropriate tone and detail level. Users can then refine or send the generated draft, with the system learning from edits to improve future generations.

Analyzes incoming emails and generated email drafts for tone, sentiment, and potential issues (e.g., overly harsh, unclear, potentially offensive) and provides feedback to users. The system can flag emails that may damage relationships or cause miscommunication, and suggest rewrites with improved tone. For outgoing drafts, it provides tone guidance before sending to help users communicate more effectively.

Enables users to export their unified inbox data (emails, Slack messages, GitHub activity, calendar events, tasks, notes) in standardized formats (JSON, CSV, PDF) for backup, compliance, or migration purposes. The system can generate compliance reports (e.g., data retention, access logs, deletion records) and supports GDPR/CCPA data subject access requests by exporting all personal data in a portable format.

Applies machine learning-based classification to incoming messages across all platforms to automatically rank and filter by urgency, relevance, and action-required status. The system learns from user behavior (which messages are opened, replied to, or marked as important) and explicit feedback to refine its classification model. Messages are tagged with priority scores and categorized (urgent, actionable, informational, spam) without requiring manual rule configuration, allowing users to focus on high-signal items first.

Automates Slack interactions by generating contextually appropriate responses to messages and threads, and automatically posting summaries or alerts to channels based on triggers from other platforms. The system monitors Slack conversations, understands thread context and mentions, and can draft replies or channel messages using the same conversational interface as email. Integration with GitHub and email allows Antispace to post relevant updates (e.g., 'PR merged', 'deadline approaching') to designated Slack channels without manual posting.

Monitors GitHub notifications (pull requests, issues, mentions, reviews) and automatically categorizes them by type and urgency, then suggests actions (review, merge, comment, close) based on PR/issue status and user role. The system understands GitHub-specific context (code diff size, review status, CI/CD results, issue labels) and can generate draft comments or review suggestions. Integration with email and Slack allows Antispace to surface critical GitHub events (failing CI, blocked PRs, assigned reviews) in the unified inbox and post summaries to Slack.

Integrates calendar events into the unified inbox and uses meeting context to enhance email and Slack message relevance. The system identifies calendar events related to incoming messages (e.g., a Slack message about a project mentioned in an upcoming meeting) and surfaces that context to the user. It can also generate meeting preparation summaries (relevant emails, GitHub PRs, Slack discussions) and suggest calendar-based task deadlines based on email or GitHub activity.

Allows users to create and manage tasks through conversational prompts (e.g., 'remind me to review John's PR by Friday') without leaving the unified inbox. The system extracts task intent, deadline, and priority from natural language, creates a task record, and integrates with calendar and reminders. Tasks can be linked to source messages (emails, Slack, GitHub) for context, and the system can generate task summaries or status updates across platforms.

Provides a unified search interface across email, Slack, GitHub, and calendar using semantic search (embedding-based) rather than keyword matching. Users can search for concepts or intents (e.g., 'discussions about the API redesign') and retrieve relevant messages from all platforms ranked by semantic relevance. The system maintains embeddings for all indexed messages and uses vector similarity to surface contextually related items even if keywords don't match exactly.

Automatically generates notes or summaries from email threads, Slack conversations, GitHub discussions, and meetings by extracting key decisions, action items, and context. The system can create meeting notes from calendar events and related messages, or generate project summaries from GitHub activity and email discussions. Notes are stored in the unified inbox and can be linked to source messages for easy reference.

Allows users to define automation rules that trigger actions across multiple platforms based on conditions from any platform. For example, a rule might automatically post a Slack message when a GitHub PR is merged, or create a task when an email with a specific keyword is received. Rules use a condition-action model where conditions can reference message content, metadata, or user behavior, and actions can span email, Slack, GitHub, calendar, and tasks.