{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_heartspace","slug":"heartspace","name":"Heartspace","type":"product","url":"https://www.heartspace.ai","page_url":"https://unfragile.ai/heartspace","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_heartspace__cap_0","uri":"capability://search.retrieval.journalist.relationship.mapping.and.discovery","name":"journalist-relationship mapping and discovery","description":"Builds a queryable database of journalist profiles, beat coverage, publication reach, and historical engagement patterns. The system likely ingests public journalist data (bylines, social profiles, publication history) and enriches it with engagement metadata (response rates, content preferences, outlet influence metrics) to enable targeted, personalized outreach. This creates a relationship graph rather than a static contact list, allowing PR teams to identify journalists most likely to cover specific story angles.","intents":["Find journalists covering our industry without manual research across 50+ publications","Identify which journalists have previously covered similar stories to ours","Understand a journalist's typical response rate and preferred communication channels before pitching","Build a long-term relationship strategy with key journalists rather than one-off pitches"],"best_for":["In-house PR teams at mid-market B2B companies","Mission-driven organizations wanting to build authentic journalist relationships","Communications teams prioritizing quality over volume in media outreach"],"limitations":["Journalist data freshness depends on update frequency — bylines and beat changes may lag 2-4 weeks","Coverage of niche or highly specialized beats may be incomplete compared to enterprise tools like Cision","Relationship scoring algorithms are proprietary — users cannot customize weighting for their specific industry context"],"requires":["Active Heartspace account with media database access","Journalist contact information (email, social handles) for relationship tracking","Historical pitch/engagement data to train personalization models (optional but improves recommendations)"],"input_types":["journalist names or publication names","story topic or beat keywords","engagement history (past pitch responses, article mentions)"],"output_types":["ranked journalist lists with contact info and engagement scores","relationship profiles with beat coverage and response patterns","personalization recommendations for outreach messaging"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_heartspace__cap_1","uri":"capability://text.generation.language.constructive.messaging.narrative.framework.and.guidance","name":"constructive-messaging narrative framework and guidance","description":"Provides editorial guidance and messaging templates that help organizations craft pitches and story angles aligned with constructive communication principles (transparency, accuracy, stakeholder consideration) rather than spin or sensationalism. The system likely uses NLP-based analysis to evaluate draft pitches against constructive communication criteria and suggests rewording that maintains persuasiveness while reducing manipulative framing. This acts as a guardrail layer between message creation and journalist outreach.","intents":["Ensure our media pitches are factually grounded and not overstating claims","Identify and remove manipulative framing from our messaging before sending to journalists","Get editorial feedback on whether our story angle serves the public interest, not just our brand","Build trust with journalists by demonstrating authentic, transparent communication"],"best_for":["ESG-conscious organizations and B-Corps wanting to align PR with values","Companies in regulated industries (healthcare, finance, energy) needing defensible messaging","Communications teams building long-term brand reputation over short-term wins"],"limitations":["Constructive communication criteria are subjective — the system's scoring may not align with all organizational values or industry norms","May flag legitimate competitive positioning as 'non-constructive,' limiting flexibility for agencies managing diverse client narratives","No built-in handling of crisis scenarios where rapid, aggressive messaging may be necessary for legal or reputational protection"],"requires":["Draft pitch or messaging content (text format)","Optional: organizational values statement or editorial guidelines for custom scoring","Understanding of constructive communication principles (transparency, accuracy, stakeholder consideration)"],"input_types":["draft press releases or pitch emails","story angles or narrative outlines","claim statements or key messages"],"output_types":["constructive communication score (0-100 scale)","specific flagged phrases with rewriting suggestions","editorial guidance on narrative framing","revised messaging templates"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_heartspace__cap_2","uri":"capability://data.processing.analysis.media.impact.measurement.and.engagement.tracking","name":"media-impact measurement and engagement tracking","description":"Tracks media coverage outcomes beyond vanity metrics (mentions, impressions) by measuring meaningful engagement signals: journalist response rates, article quality/prominence, audience sentiment, and downstream business impact (leads, brand perception shifts). The system likely integrates with media monitoring APIs to capture coverage data, correlates it with engagement metrics, and provides attribution modeling to connect media coverage to business outcomes. This enables ROI calculation for PR campaigns.","intents":["Prove that our PR efforts are driving business value, not just getting mentions","Understand which journalists and publications drive the most valuable coverage for our business","Measure whether our media strategy is reaching the right audience segments","Track sentiment and perception shifts in media coverage over time"],"best_for":["Marketing and PR teams needing to justify budget allocation to finance/executive leadership","B2B companies where media coverage drives lead generation or partnership opportunities","Organizations with defined business outcomes tied to media visibility (brand awareness, thought leadership, customer acquisition)"],"limitations":["Attribution modeling is probabilistic — cannot definitively link media coverage to business outcomes without additional data (UTM tracking, CRM integration)","Sentiment analysis accuracy varies by industry and publication type; may misclassify nuanced or technical coverage","Requires integration with CRM or analytics platforms to measure downstream business impact — standalone media metrics alone don't prove ROI","Coverage data lag (typically 24-48 hours) limits real-time campaign optimization"],"requires":["Active media monitoring integration (Heartspace's own database or third-party APIs like Meltwater, Brandwatch)","CRM or analytics platform integration for business outcome tracking (optional but necessary for true ROI measurement)","Defined KPIs and business outcomes to measure against (lead volume, brand perception, thought leadership positioning)"],"input_types":["media coverage data (articles, mentions, publication metadata)","journalist engagement data (response rates, article placement, prominence)","business outcome data (leads, customer acquisition, brand perception surveys)"],"output_types":["media impact dashboards with engagement metrics","ROI reports linking coverage to business outcomes","journalist/publication performance rankings","sentiment and perception trend analysis","attribution models showing coverage-to-outcome conversion"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_heartspace__cap_3","uri":"capability://automation.workflow.personalized.outreach.campaign.orchestration","name":"personalized-outreach campaign orchestration","description":"Automates the creation and execution of targeted media outreach campaigns by combining journalist targeting, personalized messaging, and multi-channel delivery (email, social, direct contact). The system likely uses templates and dynamic content insertion to customize pitches based on journalist profile data (beat, publication, engagement history), manages campaign scheduling and follow-up sequences, and tracks response rates across channels. This reduces manual work while maintaining personalization at scale.","intents":["Send personalized pitches to 50+ journalists without manually customizing each email","Automatically follow up with non-responders on a smart schedule without spamming","Coordinate outreach across email, social media, and direct contact in a single workflow","A/B test different pitch angles to see which resonates with different journalist segments"],"best_for":["In-house PR teams managing multiple campaigns simultaneously","Organizations wanting to scale personalized outreach without hiring additional PR staff","Companies with data-driven PR strategies that benefit from campaign performance analytics"],"limitations":["Template-based personalization may feel generic if not carefully crafted — journalists can detect mass-customized pitches","Multi-channel coordination (email, social, direct) requires careful tuning to avoid over-contact and journalist fatigue","Campaign scheduling is rule-based (e.g., 'follow up after 3 days if no response') — cannot adapt dynamically based on journalist behavior patterns","No built-in A/B testing framework — requires manual campaign variant creation and comparison"],"requires":["Journalist contact database with email addresses and social profiles","Pitch templates or messaging frameworks (can be provided by Heartspace or custom-created)","Campaign goals and success metrics (response rate targets, coverage placement targets)","Integration with email provider (Gmail, Outlook) or SMTP for delivery"],"input_types":["journalist target lists (names, publications, contact info)","pitch templates or story angles","campaign parameters (send schedule, follow-up rules, success metrics)"],"output_types":["campaign execution logs with delivery status","response tracking and engagement metrics","performance analytics (open rates, response rates, conversion to coverage)","follow-up recommendations based on engagement patterns"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_heartspace__cap_4","uri":"capability://search.retrieval.media.monitoring.and.coverage.tracking.integration","name":"media-monitoring and coverage-tracking integration","description":"Integrates with media monitoring services (likely Heartspace's own database or third-party APIs) to automatically capture, categorize, and surface relevant media coverage. The system likely uses keyword matching, publication filtering, and sentiment analysis to identify coverage related to the organization, competitors, or industry trends. Coverage data is enriched with metadata (journalist, publication, reach, sentiment) and made searchable/filterable within the Heartspace dashboard.","intents":["See all media mentions of our company and competitors in one place","Identify which journalists are writing about our industry or competitors","Track sentiment and narrative trends in media coverage over time","Set up alerts for coverage matching specific keywords or publication types"],"best_for":["Communications teams needing real-time visibility into media coverage","Competitive intelligence teams tracking competitor media presence","Organizations managing multiple brands or business units with different media profiles"],"limitations":["Coverage data lag (typically 24-48 hours) limits real-time crisis response capabilities","Sentiment analysis accuracy varies by publication type and content complexity; may misclassify nuanced coverage","Keyword-based matching can miss contextual mentions or industry-specific terminology","Media monitoring database coverage varies by geography and publication type — niche or international outlets may be underrepresented"],"requires":["Active Heartspace account with media monitoring access","Defined keywords, brand names, or publication filters for coverage tracking","Optional: competitor or industry keywords for competitive intelligence"],"input_types":["keyword or brand name searches","publication filters (industry, geography, publication type)","date range filters for historical analysis"],"output_types":["coverage lists with article links, publication metadata, and journalist info","sentiment analysis and narrative trend reports","coverage dashboards with volume and reach metrics","alert notifications for new coverage matching defined criteria"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_heartspace__cap_5","uri":"capability://text.generation.language.story.angle.and.narrative.development.assistance","name":"story-angle and narrative development assistance","description":"Helps organizations identify compelling, newsworthy story angles aligned with journalist interests and constructive communication principles. The system likely analyzes organizational news/announcements, journalist beat coverage, and current media trends to suggest story angles that are both newsworthy and authentic. This may include templates for positioning announcements, guidance on narrative framing, and suggestions for supporting data or expert commentary that strengthens the story.","intents":["Turn a product announcement into a compelling story angle that journalists will care about","Identify which aspects of our news are most relevant to different journalist beats","Find the authentic narrative in our story without resorting to spin or exaggeration","Understand what makes our story newsworthy compared to competitor announcements"],"best_for":["In-house PR teams without dedicated PR agency support","Organizations new to media relations wanting guidance on story development","Companies in technical or complex industries needing help translating announcements into accessible narratives"],"limitations":["Story angle suggestions are based on historical data and trends — may miss emerging narrative opportunities","Newsworthy criteria are subjective and vary by publication and journalist — system recommendations may not align with all editorial preferences","Requires quality input (clear announcement or news summary) to generate useful suggestions; garbage-in-garbage-out risk","Cannot replace human editorial judgment or industry expertise in identifying truly compelling angles"],"requires":["Announcement or news summary (text format)","Optional: target journalist list or publication types for angle customization","Understanding of what makes news newsworthy in your industry"],"input_types":["announcement text or news summary","product/service details or company updates","target journalist or publication information (optional)"],"output_types":["suggested story angles with newsworthiness scoring","narrative framing guidance and positioning templates","journalist/publication alignment recommendations","supporting data or expert commentary suggestions"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_heartspace__cap_6","uri":"capability://data.processing.analysis.campaign.performance.analytics.and.reporting","name":"campaign-performance analytics and reporting","description":"Generates customizable reports and dashboards showing campaign performance across metrics like response rates, coverage placement, sentiment, and business impact. The system likely aggregates data from journalist outreach, media monitoring, and optional CRM/analytics integrations to provide end-to-end campaign visibility. Reports can be customized by campaign, journalist segment, publication type, or business outcome, enabling stakeholders to understand PR effectiveness.","intents":["Show our CEO how much revenue our PR efforts generated this quarter","Compare performance across different campaigns or journalist segments","Identify which types of journalists or publications drive the best coverage for us","Create monthly reports for stakeholders on PR progress and ROI"],"best_for":["PR teams needing to justify budget and demonstrate ROI to finance/executive leadership","Organizations with multiple campaigns or business units wanting comparative performance analysis","Companies with defined business outcomes tied to media visibility (lead generation, brand awareness)"],"limitations":["Report customization may require technical configuration or support — not all users can self-serve custom report creation","Business impact attribution requires CRM/analytics integration — without it, reports show media metrics only, not ROI","Reporting latency (data may be 24-48 hours old) limits real-time campaign optimization","Benchmark data for comparison may be limited — difficult to assess whether performance is good without industry context"],"requires":["Active Heartspace account with campaign and media monitoring data","Optional: CRM or analytics platform integration for business outcome tracking","Defined KPIs and success metrics for campaigns"],"input_types":["campaign date range and filters","journalist or publication segment filters","business outcome data (optional, for ROI calculation)"],"output_types":["campaign performance dashboards with response rates, coverage metrics, sentiment","customizable reports (PDF, email, scheduled)","comparative analysis across campaigns or segments","ROI reports linking coverage to business outcomes (if CRM integrated)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_heartspace__cap_7","uri":"capability://automation.workflow.multi.stakeholder.collaboration.and.approval.workflows","name":"multi-stakeholder collaboration and approval workflows","description":"Enables multiple team members (PR, marketing, legal, executive) to collaborate on campaigns, review and approve messaging before outreach, and track changes/feedback. The system likely provides role-based access controls, comment/feedback threads on drafts, approval workflows with sign-off tracking, and version history for audit purposes. This ensures messaging alignment and compliance before journalist outreach.","intents":["Get legal review on our pitch before sending to journalists","Coordinate messaging across PR, marketing, and executive teams","Track who approved what messaging and when (for compliance/audit purposes)","Prevent unapproved or off-brand messaging from going out to journalists"],"best_for":["Larger organizations with multiple stakeholders in PR decision-making","Regulated industries (healthcare, finance) requiring legal/compliance review of messaging","Distributed teams needing asynchronous collaboration on campaigns"],"limitations":["Approval workflows can slow down campaign execution if stakeholders are slow to respond","Role-based access controls may be overly restrictive or require manual configuration for complex organizational structures","No built-in integration with legal/compliance systems — requires manual review and sign-off","Comment threads and feedback may create ambiguity if not clearly resolved before outreach"],"requires":["Multiple Heartspace user accounts with defined roles and permissions","Clear approval workflows and stakeholder responsibilities defined upfront","Integration with email or notification system for approval alerts"],"input_types":["draft pitches, press releases, or campaign messaging","reviewer/approver assignments","feedback and comments from stakeholders"],"output_types":["approval status and sign-off tracking","version history with change tracking","feedback threads and resolution status","audit logs for compliance purposes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_heartspace__cap_8","uri":"capability://planning.reasoning.journalist.preference.and.communication.style.learning","name":"journalist-preference and communication-style learning","description":"Learns individual journalist preferences (communication channel, pitch timing, content type, response patterns) from historical engagement data and uses this to optimize future outreach. The system likely tracks which pitch approaches get responses from specific journalists, identifies patterns in their coverage preferences, and suggests optimal timing/channels for future contact. This personalization improves response rates and relationship quality over time.","intents":["Know whether to email or call a journalist based on their past response patterns","Understand what type of story angle a journalist is most likely to cover","Time our pitch to when a journalist is most likely to respond","Improve our response rate by learning what works with specific journalists"],"best_for":["Organizations with ongoing relationships with the same journalists over time","PR teams wanting to optimize outreach effectiveness through data-driven personalization","Companies with sufficient historical engagement data to train preference models"],"limitations":["Preference learning requires historical engagement data — new journalists or those with limited interaction history won't have personalization","Journalist preferences change over time (beat changes, job changes, publication changes) — models may become stale without regular retraining","Preference patterns may be noisy or unreliable if based on small sample sizes (e.g., 2-3 interactions)","Privacy considerations — tracking journalist communication patterns may raise concerns if not transparent"],"requires":["Historical engagement data with journalists (past pitches, responses, coverage outcomes)","Sufficient interaction history per journalist (ideally 5+ interactions) for reliable pattern detection","Ongoing engagement tracking to continuously update preference models"],"input_types":["historical pitch and response data","journalist engagement patterns (email opens, response times, coverage outcomes)","journalist profile data (beat, publication, communication preferences)"],"output_types":["personalized outreach recommendations (channel, timing, content type)","journalist preference profiles with communication style insights","predicted response probability for different pitch approaches","optimal contact timing suggestions"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active Heartspace account with media database access","Journalist contact information (email, social handles) for relationship tracking","Historical pitch/engagement data to train personalization models (optional but improves recommendations)","Draft pitch or messaging content (text format)","Optional: organizational values statement or editorial guidelines for custom scoring","Understanding of constructive communication principles (transparency, accuracy, stakeholder consideration)","Active media monitoring integration (Heartspace's own database or third-party APIs like Meltwater, Brandwatch)","CRM or analytics platform integration for business outcome tracking (optional but necessary for true ROI measurement)","Defined KPIs and business outcomes to measure against (lead volume, brand perception, thought leadership positioning)","Journalist contact database with email addresses and social profiles"],"failure_modes":["Journalist data freshness depends on update frequency — bylines and beat changes may lag 2-4 weeks","Coverage of niche or highly specialized beats may be incomplete compared to enterprise tools like Cision","Relationship scoring algorithms are proprietary — users cannot customize weighting for their specific industry context","Constructive communication criteria are subjective — the system's scoring may not align with all organizational values or industry norms","May flag legitimate competitive positioning as 'non-constructive,' limiting flexibility for agencies managing diverse client narratives","No built-in handling of crisis scenarios where rapid, aggressive messaging may be necessary for legal or reputational protection","Attribution modeling is probabilistic — cannot definitively link media coverage to business outcomes without additional data (UTM tracking, CRM integration)","Sentiment analysis accuracy varies by industry and publication type; may misclassify nuanced or technical coverage","Requires integration with CRM or analytics platforms to measure downstream business impact — standalone media metrics alone don't prove ROI","Coverage data lag (typically 24-48 hours) limits real-time campaign optimization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:30.893Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=heartspace","compare_url":"https://unfragile.ai/compare?artifact=heartspace"}},"signature":"7SnLQFTkvznFP3bV7+W0V5h9II2C6hy9bHvX2SpukG86YyBur03fuTIRzFcJvkYtSUa4cQB/R2+G+F3YiFbGAA==","signedAt":"2026-06-19T16:59:39.022Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/heartspace","artifact":"https://unfragile.ai/heartspace","verify":"https://unfragile.ai/api/v1/verify?slug=heartspace","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"}}