Rizemail

Automatically generates concise summaries of incoming emails using language models while preserving message context within the user's existing email client interface. The system intercepts incoming messages, extracts content and metadata (sender, subject, threading), processes through an LLM summarization pipeline, and injects summaries as inline previews or separate summary threads without requiring email migration or client switching. Architecture appears to use email protocol integration (IMAP/API hooks) to capture messages pre-display and return augmented content to the native inbox view.

Classifies incoming emails into priority tiers (critical, important, low-priority) using learned patterns from user behavior and email content features, then surfaces high-priority messages while batching or de-emphasizing low-priority ones. The system likely uses a multi-feature classifier combining sender reputation, subject line keywords, content semantic analysis, and implicit user signals (open rate, response time) to assign priority scores. Messages are then reordered or visually grouped in the inbox to surface actionable items first.

Processes email content for summarization and analysis while maintaining cryptographic guarantees that Rizemail servers cannot access plaintext message content. The system likely uses client-side encryption (encrypt-before-send pattern) where summarization happens on user's device or in a secure enclave, with only encrypted content transmitted to servers. Alternatively, uses homomorphic encryption or secure multi-party computation to perform classification/summarization on encrypted data without decryption on the server side.

Consolidates email from multiple providers (Gmail, Outlook, Yahoo, custom IMAP servers) into a single unified inbox view with consistent summarization and priority ranking across all accounts. The system uses provider-specific OAuth/IMAP connectors to fetch messages from each account, normalizes email format and metadata to a common schema, applies summarization and classification pipelines uniformly, and renders results in a unified UI. Architecture likely uses a message queue (Kafka, RabbitMQ) to handle asynchronous fetching and processing across multiple accounts without blocking on any single provider.

Analyzes incoming email content and context (sender, subject, conversation history) to suggest relevant reply templates or auto-generate draft responses using language models. The system extracts intent from the incoming message (question, request, announcement, etc.), retrieves matching templates from a library (user-created or pre-built), and optionally generates a personalized draft response that the user can edit before sending. Architecture likely uses intent classification + retrieval-augmented generation (RAG) to match templates, then fine-tuned LLM for draft generation.

Aggregates incoming emails over a user-defined time window (e.g., hourly, daily, weekly) and delivers a single consolidated digest containing summaries of all messages received during that period. The system batches messages by category (work, personal, notifications), applies summarization to each batch, and delivers via email, push notification, or in-app notification at scheduled times. Architecture uses a message queue and scheduler (cron-like) to batch messages, apply summarization in bulk (more efficient than per-message processing), and trigger delivery at specified intervals.

Builds a per-sender trust profile based on historical interaction patterns (response rate, email frequency, content quality, domain reputation) and assigns a trust score that influences priority ranking and summarization depth. The system tracks metrics like user response latency to sender, frequency of emails from that sender, whether emails are typically read or archived, and external signals (domain age, SPF/DKIM validation, spam report history). High-trust senders get more prominent placement and detailed summaries; low-trust senders are batched or summarized more aggressively.

Detects attachments in emails and incorporates attachment metadata (filename, type, size) and content analysis (OCR for images, text extraction from PDFs) into email summarization. The system identifies emails with actionable attachments (contracts, invoices, documents) and adjusts summarization to highlight attachment relevance. For image attachments, uses OCR to extract text; for PDFs, extracts key sections; for other types, flags presence and type. Summarization explicitly mentions attachment content when relevant to the email intent.

Reconstructs email conversation threads and generates summaries that span multiple messages, capturing the full context and decision history rather than summarizing each message in isolation. The system uses email headers (In-Reply-To, References) and content similarity to identify related messages, orders them chronologically, and applies a multi-message summarization algorithm that extracts key decisions, action items, and open questions across the entire thread. Output includes thread-level summary plus per-message summaries for quick scanning.

Automatically detects and categorizes newsletters, marketing emails, and subscription-based messages, then provides one-click unsubscribe or bulk management options. The system uses heuristics (List-Unsubscribe header, marketing email patterns, sender domain reputation) to identify newsletters, groups them by category (news, promotions, social updates, etc.), and offers bulk actions (unsubscribe all, mute for 30 days, archive all). Integrates with email provider's unsubscribe mechanisms (Gmail's unsubscribe button, Outlook's Focused Inbox) for seamless removal.