{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_rizemail","slug":"rizemail","name":"Rizemail","type":"product","url":"https://www.rizemail.app","page_url":"https://unfragile.ai/rizemail","categories":["automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_rizemail__cap_0","uri":"capability://text.generation.language.inbox.native.email.summarization.with.ai.context.preservation","name":"inbox-native email summarization with ai context preservation","description":"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.","intents":["I want to quickly scan what 50+ daily emails are about without opening each one","I need summaries that preserve critical details from long email chains","I want AI summarization without leaving my existing email client","I need to reduce time spent on email triage and context-switching"],"best_for":["Busy professionals receiving 50+ emails daily across mixed content types","Knowledge workers managing complex email threads with multiple stakeholders","Teams using Gmail, Outlook, or other major email providers who want minimal friction adoption"],"limitations":["Summarization quality degrades on nuanced business correspondence—loses critical context in emails requiring interpretation or negotiation","No customization for summarization depth, style, or priority weighting means one-size-fits-all approach fails for power users with heterogeneous email patterns","Transactional emails (receipts, confirmations) summarize well but domain-specific emails (legal, technical specs) may oversimplify critical details","Latency overhead from LLM processing adds delay to inbox refresh cycles—likely 2-5 seconds per batch of messages"],"requires":["Active email account on supported provider (Gmail, Outlook, or equivalent IMAP-compatible service)","Internet connectivity for cloud-based LLM inference","Browser or email client with JavaScript support for integration layer"],"input_types":["email messages (full headers, body, attachments metadata)","email threads (conversation history)","sender metadata (domain, contact history)"],"output_types":["text summaries (2-3 sentence abstracts)","structured metadata (priority flags, category tags)","inline preview text in email client UI"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rizemail__cap_1","uri":"capability://data.processing.analysis.selective.email.filtering.and.priority.ranking.with.ai.classification","name":"selective email filtering and priority ranking with ai classification","description":"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.","intents":["I want critical emails (from my boss, key clients) to surface first without manual rules","I need to batch transactional emails (receipts, notifications) separately from business correspondence","I want the system to learn what matters to me over time without explicit configuration","I need to reduce cognitive load by hiding low-priority messages from my primary inbox view"],"best_for":["Professionals with mixed email sources (internal, external, automated notifications) who need smart triage","Users who receive high email volume but lack time to set up manual filtering rules","Teams where email priority varies by context (project phase, stakeholder, time of day)"],"limitations":["Limited customization for priority rules—one-size-fits-all classifier may misclassify domain-specific emails (e.g., vendor communications marked low-priority when they're critical for procurement)","Requires sufficient email history to train classifier—new users get generic priority ranking until system observes behavior patterns (likely 1-2 weeks of data)","No explicit user feedback loop documented—system may not adapt if user behavior changes or priorities shift mid-project","False negatives (critical emails marked low-priority) create risk for time-sensitive communications"],"requires":["Active email account with sufficient message history (50+ emails minimum for meaningful classification)","Rizemail account with email provider authentication","Implicit permission to analyze email content and metadata for classification training"],"input_types":["email sender address and reputation","email subject line and body text","email metadata (timestamp, thread depth, attachment presence)","user interaction signals (open/read status, response latency)"],"output_types":["priority score (0-1 or categorical: critical/important/low)","visual inbox grouping or reordering","filter tags or labels applied to messages"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rizemail__cap_2","uri":"capability://safety.moderation.end.to.end.encrypted.email.content.processing.with.zero.knowledge.architecture","name":"end-to-end encrypted email content processing with zero-knowledge architecture","description":"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.","intents":["I need AI email summarization but cannot expose sensitive business/legal content to third-party servers","I want compliance with data residency requirements (GDPR, HIPAA) for email processing","I need cryptographic proof that my email content is not stored or analyzed by the service provider","I want to use AI email tools without trusting the vendor with plaintext access to my messages"],"best_for":["Enterprise users in regulated industries (finance, healthcare, legal) handling sensitive correspondence","Organizations with strict data governance policies prohibiting plaintext email transmission to third parties","Privacy-conscious professionals who view email content as confidential intellectual property"],"limitations":["Zero-knowledge architecture adds computational overhead—summarization latency likely 5-10x higher than plaintext processing due to encryption/decryption cycles","Homomorphic encryption or secure enclaves limit model complexity—likely uses smaller, less capable LLMs than cloud-based alternatives","Client-side processing requires local compute resources—may be slow on mobile devices or older hardware","Debugging and monitoring become difficult without plaintext access—harder to detect and fix summarization errors","Compliance guarantees depend on correct implementation—any bug in encryption layer undermines security claims"],"requires":["Modern browser or email client with WebCrypto or equivalent cryptographic APIs","Sufficient local compute for encryption/decryption (likely 100-500ms per message on modern hardware)","Trust in Rizemail's cryptographic implementation and third-party security audits"],"input_types":["plaintext email messages (encrypted client-side before transmission)","email metadata (encrypted or anonymized)"],"output_types":["encrypted summaries (decrypted only on user's device)","priority scores (computed on encrypted data or returned encrypted)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rizemail__cap_3","uri":"capability://tool.use.integration.multi.provider.email.account.aggregation.and.unified.summarization","name":"multi-provider email account aggregation and unified summarization","description":"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.","intents":["I manage multiple email accounts (work, personal, client-specific) and want to see all summaries in one place","I want consistent AI summarization across Gmail, Outlook, and other providers without switching between tools","I need to prioritize emails across all my accounts to focus on what matters most","I want to reduce context-switching between multiple email clients"],"best_for":["Professionals managing 2+ email accounts across different providers (work Gmail + personal Outlook, etc.)","Freelancers and consultants with client-specific email addresses","Teams using hybrid email infrastructure (some on Google Workspace, some on Microsoft 365)"],"limitations":["Synchronization latency across providers—unified view may be 30-60 seconds behind real-time if using polling instead of webhooks","Provider API rate limits constrain update frequency—may batch updates to 5-15 minute intervals for accounts with high message volume","OAuth token management complexity—requires secure storage and refresh logic for multiple provider credentials","Inconsistent email metadata across providers (Gmail labels vs. Outlook categories) may not normalize cleanly, causing classification errors","IMAP-only accounts lack real-time push notifications—requires polling, adding latency and server load"],"requires":["OAuth credentials or IMAP credentials for each email account","Rizemail account with multi-account linking feature enabled","Internet connectivity to maintain connections to multiple email providers simultaneously"],"input_types":["email accounts from Gmail, Outlook, Yahoo, or IMAP-compatible providers","OAuth tokens or IMAP credentials for each account","user preferences for account priority or grouping"],"output_types":["unified inbox view with messages from all accounts","cross-account priority ranking","aggregated summary statistics (total unread, priority breakdown)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rizemail__cap_4","uri":"capability://text.generation.language.contextual.email.template.suggestions.and.smart.reply.generation","name":"contextual email template suggestions and smart reply generation","description":"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.","intents":["I want to reply to common email types (meeting requests, status updates) with minimal typing","I need smart reply suggestions that match my tone and communication style","I want to create reusable email templates that the system suggests automatically","I need to draft responses faster without sacrificing personalization"],"best_for":["High-volume email users (50+ emails/day) who send repetitive responses","Customer-facing roles (support, sales) with standardized reply patterns","Non-native English speakers who want assistance with email composition"],"limitations":["Template suggestions may be generic or tone-deaf if intent classification is inaccurate—requires user review and editing","Auto-generated drafts lack context awareness for nuanced situations—may suggest inappropriate responses to sensitive emails","No learning from user edits—system doesn't improve suggestions based on which drafts users accept/reject","Limited to English or major languages—multilingual email handling likely weak","Privacy concern: storing user-created templates requires secure storage and may expose sensitive communication patterns"],"requires":["Rizemail account with template library feature","Sufficient email history to infer user communication patterns (optional, for personalization)","User-created templates or access to pre-built template library"],"input_types":["incoming email message (sender, subject, body, conversation history)","user-created or pre-built email templates","user communication style preferences (tone, formality level)"],"output_types":["ranked list of template suggestions","auto-generated draft response text","confidence scores for suggestions"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rizemail__cap_5","uri":"capability://automation.workflow.scheduled.email.digest.and.batched.summary.delivery","name":"scheduled email digest and batched summary delivery","description":"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.","intents":["I want to check email on my schedule, not constantly throughout the day","I need a daily or weekly summary of all emails instead of real-time notifications","I want to batch low-priority emails and review them in one sitting","I need to reduce email-driven interruptions and context-switching"],"best_for":["Deep-work professionals who need uninterrupted focus time","Users in asynchronous-first teams or distributed organizations","People managing email overload who want to batch-process messages"],"limitations":["Delayed notification of time-sensitive emails—digest batching means critical messages may not surface for hours","Requires user discipline to actually review digests at scheduled times—risk of digest becoming another backlog","Summarization of large batches (50+ emails) may lose important details due to compression","No real-time alerting for truly urgent emails—requires manual override or whitelist of critical senders","Digest delivery format (email, push) may not be ideal for all use cases—email digest in email client is redundant"],"requires":["Rizemail account with digest scheduling feature","User-defined schedule preferences (frequency, time of day, batching rules)","Delivery channel (email, push notification, or in-app)"],"input_types":["incoming emails (accumulated over time window)","user schedule preferences (frequency, time, batching rules)","priority/whitelist rules for urgent emails"],"output_types":["consolidated digest email or notification","batched summaries grouped by category","metadata (total count, priority breakdown)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rizemail__cap_6","uri":"capability://data.processing.analysis.sender.reputation.and.trust.scoring.with.historical.analysis","name":"sender reputation and trust scoring with historical analysis","description":"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.","intents":["I want emails from my key contacts (boss, important clients) to always surface first","I need the system to learn which senders matter to me based on my behavior","I want to deprioritize emails from low-engagement senders automatically","I need to identify and filter out suspicious or low-reputation senders"],"best_for":["Professionals with established email relationships and clear sender hierarchies","Users who want implicit priority learning without manual configuration","Teams where sender importance is consistent over time"],"limitations":["Requires sufficient historical data to build accurate trust profiles—new senders get generic scoring until interaction history accumulates","Trust scores may become stale if user priorities shift (e.g., new project with previously low-priority sender)","Domain reputation signals (SPF, DKIM) are coarse-grained and may misclassify legitimate senders from shared domains","No explicit user feedback mechanism documented—system may not adapt if user manually marks trusted senders as spam or vice versa","Privacy concern: tracking detailed sender interaction patterns requires storing user behavior data"],"requires":["Sufficient email history (100+ messages minimum) to build meaningful sender profiles","Rizemail account with sender reputation feature enabled","Permission to analyze sender metadata and user interaction patterns"],"input_types":["sender email address and domain","user interaction history (response rate, read status, archive behavior)","email frequency and content patterns from sender","external reputation signals (domain age, SPF/DKIM, spam reports)"],"output_types":["trust score (0-1 or categorical: trusted/neutral/suspicious)","priority boost or penalty applied to sender's emails","summarization depth adjustment based on trust level"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rizemail__cap_7","uri":"capability://data.processing.analysis.attachment.detection.and.content.aware.summarization","name":"attachment detection and content-aware summarization","description":"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.","intents":["I want summaries that mention important attachments (contracts, invoices, documents)","I need to quickly identify emails with actionable attachments without opening them","I want OCR text from image attachments to be searchable and summarized","I need to know if an email has attachments and what they contain without downloading"],"best_for":["Professionals receiving document-heavy emails (contracts, invoices, reports)","Teams managing procurement, legal, or financial workflows with attachment-based communication","Users who need to triage emails based on attachment presence and type"],"limitations":["OCR quality varies by image resolution and quality—may fail on low-quality scans or handwritten documents","PDF extraction may lose formatting and structure—complex layouts (tables, multi-column) may not extract cleanly","Large attachments (10+ MB) may timeout during processing—requires size limits or async processing","Attachment content analysis adds latency—summarization may be delayed for emails with large or complex attachments","Privacy concern: processing attachment content (especially documents) requires secure handling and may expose sensitive data"],"requires":["Rizemail account with attachment processing feature","OCR engine (likely Tesseract or cloud-based) for image text extraction","PDF parsing library (likely PyPDF2 or similar) for document extraction","File size limits (likely 10-50 MB per attachment)"],"input_types":["email attachments (images, PDFs, documents)","attachment metadata (filename, MIME type, size)","email body text (for context)"],"output_types":["attachment summary (type, size, extracted text preview)","attachment relevance score (how relevant to email intent)","searchable OCR text from images","key sections extracted from PDFs"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rizemail__cap_8","uri":"capability://text.generation.language.conversation.threading.and.multi.message.context.aggregation","name":"conversation threading and multi-message context aggregation","description":"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.","intents":["I want a summary of the entire email conversation, not just the latest message","I need to understand the decision history and context in a long email thread","I want to quickly identify action items and open questions across a conversation","I need to catch up on a thread I've been away from without reading every message"],"best_for":["Professionals managing complex multi-party email conversations","Teams with long decision-making threads that require full context","Users returning from time away who need to catch up on ongoing discussions"],"limitations":["Thread reconstruction may fail if email headers are malformed or missing—some forwarded emails may not thread correctly","Multi-message summarization may lose nuance or conflicting viewpoints—compression of long threads inevitably loses detail","Summarization of 20+ message threads may be too compressed to be useful—requires user to read full thread anyway","Action item extraction is error-prone—may miss implicit tasks or misidentify completed vs. pending items","No explicit tracking of who said what—summary may obscure important attribution (e.g., which stakeholder made a decision)"],"requires":["Rizemail account with conversation threading feature","Email headers (In-Reply-To, References) for thread reconstruction","Multi-message LLM summarization capability"],"input_types":["email messages in a thread (multiple messages with headers)","email metadata (sender, timestamp, subject)","conversation history (full thread)"],"output_types":["thread-level summary (overall context and decisions)","per-message summaries (for quick scanning)","extracted action items and open questions","participant list and roles"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rizemail__cap_9","uri":"capability://automation.workflow.smart.unsubscribe.and.newsletter.categorization.with.bulk.management","name":"smart unsubscribe and newsletter categorization with bulk management","description":"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.","intents":["I want to unsubscribe from newsletters and marketing emails in bulk without opening each one","I need to categorize newsletters separately from business emails","I want to mute newsletters temporarily without fully unsubscribing","I need to reduce email volume from subscriptions I've forgotten about"],"best_for":["Users with high email volume from newsletters and marketing lists","Professionals who want to separate signal (business emails) from noise (newsletters)","People who have accumulated many subscriptions over time and want to clean up"],"limitations":["Newsletter detection may have false positives (legitimate business emails marked as newsletters) or false negatives (newsletters not detected)","Unsubscribe mechanisms vary by provider—some newsletters don't include List-Unsubscribe header, requiring manual unsubscribe","Bulk unsubscribe is irreversible—no undo mechanism if user unsubscribes from something they actually wanted","Mute feature requires server-side state tracking—may not sync across devices","Some newsletters use obfuscated unsubscribe links or require account login—automation may fail"],"requires":["Rizemail account with newsletter management feature","Email provider support for unsubscribe mechanisms (Gmail, Outlook, etc.)","List-Unsubscribe header detection and parsing"],"input_types":["incoming emails (to detect newsletter patterns)","email headers (List-Unsubscribe, X-Mailer, etc.)","sender domain and reputation data"],"output_types":["newsletter categorization (news, promotions, social, etc.)","bulk unsubscribe actions","mute/snooze options","subscription summary (count by category)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active email account on supported provider (Gmail, Outlook, or equivalent IMAP-compatible service)","Internet connectivity for cloud-based LLM inference","Browser or email client with JavaScript support for integration layer","Active email account with sufficient message history (50+ emails minimum for meaningful classification)","Rizemail account with email provider authentication","Implicit permission to analyze email content and metadata for classification training","Modern browser or email client with WebCrypto or equivalent cryptographic APIs","Sufficient local compute for encryption/decryption (likely 100-500ms per message on modern hardware)","Trust in Rizemail's cryptographic implementation and third-party security audits","OAuth credentials or IMAP credentials for each email account"],"failure_modes":["Summarization quality degrades on nuanced business correspondence—loses critical context in emails requiring interpretation or negotiation","No customization for summarization depth, style, or priority weighting means one-size-fits-all approach fails for power users with heterogeneous email patterns","Transactional emails (receipts, confirmations) summarize well but domain-specific emails (legal, technical specs) may oversimplify critical details","Latency overhead from LLM processing adds delay to inbox refresh cycles—likely 2-5 seconds per batch of messages","Limited customization for priority rules—one-size-fits-all classifier may misclassify domain-specific emails (e.g., vendor communications marked low-priority when they're critical for procurement)","Requires sufficient email history to train classifier—new users get generic priority ranking until system observes behavior patterns (likely 1-2 weeks of data)","No explicit user feedback loop documented—system may not adapt if user behavior changes or priorities shift mid-project","False negatives (critical emails marked low-priority) create risk for time-sensitive communications","Zero-knowledge architecture adds computational overhead—summarization latency likely 5-10x higher than plaintext processing due to encryption/decryption cycles","Homomorphic encryption or secure enclaves limit model complexity—likely uses smaller, less capable LLMs than cloud-based alternatives","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:33.095Z","last_scraped_at":"2026-04-05T13:23:42.560Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=rizemail","compare_url":"https://unfragile.ai/compare?artifact=rizemail"}},"signature":"YxeWQEOXgQ30zVTrXQYZZ1HblvwP7rJ30TcgtRK/HK2m064/qMCS2SGWR/l4edv3nt3vAfZvcaDKaA7svd4JBw==","signedAt":"2026-06-21T16:57:05.719Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rizemail","artifact":"https://unfragile.ai/rizemail","verify":"https://unfragile.ai/api/v1/verify?slug=rizemail","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}