{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_empy-ai","slug":"empy-ai","name":"Empy.ai","type":"product","url":"https://empy.ai","page_url":"https://unfragile.ai/empy-ai","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_empy-ai__cap_0","uri":"capability://data.processing.analysis.real.time.slack.message.tone.analysis.with.empathy.scoring","name":"real-time slack message tone analysis with empathy scoring","description":"Analyzes incoming Slack messages in real-time using NLP-based sentiment and tone classification to generate empathy scores, likely leveraging transformer-based language models fine-tuned on communication datasets. The system integrates directly with Slack's Events API to intercept messages as they're posted, classify them against empathy/tone dimensions (e.g., directness, emotional awareness, inclusivity), and surface scores to users without requiring manual message submission or external tools.","intents":["I want to understand if my team's Slack messages are becoming less empathetic over time","I need real-time feedback when I'm about to send a message that might come across as harsh or dismissive","I want to identify which team members or channels have the most problematic communication patterns"],"best_for":["Remote-first engineering and product teams with existing psychological safety initiatives","HR and people operations teams managing distributed workforces","Organizations with explicit communication culture goals and leadership buy-in"],"limitations":["Tone classification is context-dependent and may misinterpret sarcasm, cultural communication styles, or technical jargon as negative","Real-time analysis adds latency to message delivery or requires async processing that delays feedback","No distinction between internal team channels and public/external channels — may flag appropriate directness in technical discussions as low empathy","Accuracy degrades on short messages, emoji-heavy communication, or non-English languages"],"requires":["Slack workspace with admin permissions to install custom app","Slack Events API access (requires Slack App with message:read scope)","Active Empy.ai subscription and API credentials","Minimum 10 team members for meaningful pattern detection"],"input_types":["Slack message text","Message metadata (sender, channel, timestamp, thread context)"],"output_types":["Empathy score (numeric, likely 0-100 scale)","Tone classification tags (e.g., 'direct', 'dismissive', 'supportive')","Structured analytics data (JSON/CSV for reporting)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_empy-ai__cap_1","uri":"capability://data.processing.analysis.team.communication.pattern.aggregation.and.trend.reporting","name":"team communication pattern aggregation and trend reporting","description":"Aggregates individual message tone scores across team members, channels, and time periods to generate dashboards and reports showing communication health trends. The system likely uses time-series aggregation (daily/weekly/monthly bucketing) and statistical analysis to identify which teams, individuals, or channels are trending toward lower empathy, enabling managers to spot systemic communication issues before they escalate into team dysfunction.","intents":["I want to see which of my teams has the most empathetic communication culture","I need to track whether our communication improvements are actually working over time","I want to identify specific channels or team members who need coaching on empathetic communication"],"best_for":["Engineering managers and team leads overseeing distributed teams","People operations and HR teams measuring culture metrics","Organizations with quarterly or annual communication culture initiatives"],"limitations":["Aggregation masks individual context — a channel with low empathy scores might be a technical debugging channel where directness is appropriate","Trend analysis requires minimum historical data (likely 2-4 weeks) before patterns become statistically meaningful","No causal analysis — reports show correlation (low empathy scores) but not root cause (e.g., deadline pressure, unclear requirements, personality conflicts)","Privacy concerns if reports are shared widely — employees may feel surveilled if individual scores are visible to non-managers"],"requires":["Minimum 2-4 weeks of message history for baseline trend detection","Slack workspace with 10+ active members for statistical significance","Dashboard/reporting access (likely web-based or Slack app interface)"],"input_types":["Aggregated empathy scores from individual messages","Message metadata (sender, channel, timestamp)"],"output_types":["Time-series trend data (JSON/CSV)","Dashboard visualizations (charts, heatmaps)","Summary reports (PDF or Slack message format)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_empy-ai__cap_2","uri":"capability://text.generation.language.contextual.empathy.coaching.and.message.rewrite.suggestions","name":"contextual empathy coaching and message rewrite suggestions","description":"Generates alternative phrasings or coaching suggestions for messages flagged as low-empathy, using generative language models to propose more empathetic rewrites while preserving the original intent. The system likely uses prompt engineering or fine-tuned models to suggest tone adjustments (e.g., adding acknowledgment of impact, softening directness, including emotional validation) and may surface these suggestions pre-send (as a Slack bot) or post-send (as feedback).","intents":["I want suggestions for how to rephrase my message to be more empathetic before I send it","I want to learn what makes a message feel dismissive so I can improve my communication style","I want to coach my team on empathetic communication by showing them examples of better phrasings"],"best_for":["Individual contributors and managers who want to improve their communication in real-time","Teams with explicit communication coaching programs or mentorship initiatives","Organizations where psychological safety is a stated value but communication skills are underdeveloped"],"limitations":["Rewrite suggestions may change the tone so much that the original message becomes unclear or loses necessary directness (e.g., technical feedback becomes vague)","Generative suggestions can hallucinate or produce awkward phrasings that don't match team communication norms","No learning loop — suggestions don't adapt to individual communication style or team preferences over time","May be perceived as patronizing or intrusive if surfaced too aggressively or to senior team members"],"requires":["Generative language model access (likely OpenAI GPT or similar)","Slack bot integration with message interception capability","User opt-in for pre-send suggestions (privacy/consent)"],"input_types":["Original message text","Empathy score and tone classification from analysis engine","Optional: channel context, recipient information"],"output_types":["Rewritten message suggestions (1-3 alternatives)","Coaching explanation (why the original was flagged, what changed)","Confidence score for suggestion quality"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_empy-ai__cap_3","uri":"capability://tool.use.integration.slack.events.api.integration.with.message.interception.and.classification.pipeline","name":"slack events api integration with message interception and classification pipeline","description":"Implements a real-time message processing pipeline that hooks into Slack's Events API to intercept messages as they're posted, routes them through NLP classification models, and stores results in a database for analytics and reporting. The architecture likely uses async message queues (e.g., Kafka, RabbitMQ) to decouple message ingestion from classification to prevent blocking Slack's message delivery, with fallback handling for failed classifications.","intents":["I want to analyze all messages in my Slack workspace without requiring manual submission or external tools","I want the analysis to happen in real-time without adding latency to message delivery","I want to ensure that all messages are consistently classified using the same model and criteria"],"best_for":["Organizations with 100+ daily active Slack users where batch processing would be too slow","Teams that want continuous, automated monitoring rather than on-demand analysis","Enterprises with strict data governance requirements (on-premise or VPC deployment)"],"limitations":["Real-time processing adds infrastructure complexity and cost (message queues, databases, compute for classification)","Slack Events API has rate limits and delivery guarantees (at-least-once, not exactly-once) — may require deduplication logic","Message interception requires workspace-level admin permissions, creating security and privacy concerns","Classification latency (even async) means suggestions or feedback may arrive seconds after message is posted, reducing effectiveness"],"requires":["Slack workspace admin access to install custom app","Slack Events API credentials and webhook configuration","Backend infrastructure (cloud or on-premise) to run message processing pipeline","Database for storing classification results and analytics data","NLP model serving infrastructure (GPU or CPU-based)"],"input_types":["Slack Events API webhook payloads (message.posted events)","Message text, metadata (sender, channel, timestamp, thread context)"],"output_types":["Classification results (empathy score, tone tags)","Stored analytics data (database records)","Optional: real-time feedback to Slack (bot messages, reactions)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_empy-ai__cap_4","uri":"capability://safety.moderation.privacy.aware.message.storage.and.data.retention.with.compliance.controls","name":"privacy-aware message storage and data retention with compliance controls","description":"Stores message text and classification results in a database with configurable retention policies, encryption, and access controls to address privacy concerns around message surveillance. The system likely implements field-level encryption for message content, role-based access control (RBAC) for who can view analytics, and automated data deletion based on retention policies (e.g., delete raw messages after 30 days, keep only aggregated scores).","intents":["I want to ensure that raw message text is not stored longer than necessary for compliance reasons","I want to control who in my organization can see individual message scores vs. only aggregated team metrics","I want to ensure that message data is encrypted and not accessible to Empy.ai employees"],"best_for":["Organizations in regulated industries (finance, healthcare) with strict data retention requirements","Companies with strong privacy cultures or employee privacy advocates","Teams that need to address employee concerns about surveillance before rolling out the tool"],"limitations":["Encryption and access controls add operational complexity and potential performance overhead","Retention policies may conflict with analytics needs — deleting raw messages limits ability to investigate specific communication issues","No built-in audit logging for who accessed what data — requires integration with external audit systems","Privacy controls are only as strong as the organization's enforcement — misconfigured RBAC can expose sensitive data"],"requires":["Database with encryption-at-rest support (e.g., AWS RDS with encryption, PostgreSQL with pgcrypto)","Identity and access management system (SAML, OAuth, or Slack's identity)","Compliance framework (GDPR, HIPAA, SOC 2) if required by organization"],"input_types":["Message text and metadata from Slack","User identity and role information"],"output_types":["Encrypted message storage","Access-controlled analytics views","Audit logs (optional)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_empy-ai__cap_5","uri":"capability://data.processing.analysis.channel.specific.and.role.based.tone.analysis.filtering","name":"channel-specific and role-based tone analysis filtering","description":"Applies different empathy scoring criteria or thresholds based on channel type (e.g., #engineering-debugging vs. #general) or user role (e.g., managers vs. individual contributors), recognizing that communication norms vary across contexts. The system likely uses metadata-based routing to apply different models or scoring weights, allowing organizations to avoid flagging appropriate directness in technical channels while still catching genuinely problematic communication in social or all-hands channels.","intents":["I want to avoid false positives where technical directness in #engineering is flagged as low empathy","I want stricter empathy standards for manager-to-report communication than peer-to-peer communication","I want to customize tone analysis for different team cultures (e.g., startup vs. enterprise communication norms)"],"best_for":["Large organizations with diverse team cultures and communication norms across departments","Technical teams where directness and efficiency are valued in certain contexts","Organizations with explicit role-based communication expectations (e.g., managers should be more empathetic than engineers)"],"limitations":["Requires manual configuration of channel and role rules — no automatic detection of appropriate communication norms","Over-customization can lead to inconsistent standards across the organization and confusion about what's acceptable","No learning loop — rules don't adapt as team culture evolves","May enable problematic behavior if rules are misconfigured (e.g., allowing low empathy in channels where it shouldn't be)"],"requires":["Channel metadata from Slack (channel name, topic, description)","User role information (from Slack workspace roles or external identity system)","Configuration interface to define rules and thresholds per channel/role"],"input_types":["Message text and metadata","Channel type and name","Sender role or title"],"output_types":["Adjusted empathy scores based on context","Context-aware tone classification","Filtered analytics (only flagging issues in relevant contexts)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Slack workspace with admin permissions to install custom app","Slack Events API access (requires Slack App with message:read scope)","Active Empy.ai subscription and API credentials","Minimum 10 team members for meaningful pattern detection","Minimum 2-4 weeks of message history for baseline trend detection","Slack workspace with 10+ active members for statistical significance","Dashboard/reporting access (likely web-based or Slack app interface)","Generative language model access (likely OpenAI GPT or similar)","Slack bot integration with message interception capability","User opt-in for pre-send suggestions (privacy/consent)"],"failure_modes":["Tone classification is context-dependent and may misinterpret sarcasm, cultural communication styles, or technical jargon as negative","Real-time analysis adds latency to message delivery or requires async processing that delays feedback","No distinction between internal team channels and public/external channels — may flag appropriate directness in technical discussions as low empathy","Accuracy degrades on short messages, emoji-heavy communication, or non-English languages","Aggregation masks individual context — a channel with low empathy scores might be a technical debugging channel where directness is appropriate","Trend analysis requires minimum historical data (likely 2-4 weeks) before patterns become statistically meaningful","No causal analysis — reports show correlation (low empathy scores) but not root cause (e.g., deadline pressure, unclear requirements, personality conflicts)","Privacy concerns if reports are shared widely — employees may feel surveilled if individual scores are visible to non-managers","Rewrite suggestions may change the tone so much that the original message becomes unclear or loses necessary directness (e.g., technical feedback becomes vague)","Generative suggestions can hallucinate or produce awkward phrasings that don't match team communication norms","builder identity is not verified yet","no observed match outcomes 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