{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_rely-io","slug":"rely-io","name":"Rely.io","type":"product","url":"https://www.rely.io","page_url":"https://unfragile.ai/rely-io","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_rely-io__cap_0","uri":"capability://reliability.ai.driven.incident.correlation.and.deduplication","name":"ai-driven incident correlation and deduplication","description":"Automatically groups related alerts and incidents across multiple sources into unified incidents, reducing noise and preventing duplicate notifications. Uses machine learning to identify patterns and correlations that human operators might miss.","intents":["I want to stop getting flooded with duplicate alerts about the same issue","I need to understand which alerts are actually related to the same root problem","I want to reduce the number of incidents my team has to manually triage"],"best_for":["engineering teams with high alert volume","organizations using multiple monitoring tools","teams struggling with alert fatigue"],"limitations":["requires sufficient historical incident data to train correlation models","may miss novel incident patterns not seen before"],"requires":["integration with monitoring/alerting systems","incident history data"],"input_types":["alert metadata","alert timestamps","alert sources","alert descriptions"],"output_types":["correlated incident groups","deduplicated incident records"],"categories":["reliability","incident management","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_1","uri":"capability://reliability.root.cause.analysis.and.recommendation.generation","name":"root cause analysis and recommendation generation","description":"Analyzes incident data to identify likely root causes and suggests remediation steps based on historical patterns and system context. Provides actionable recommendations to engineers without requiring deep investigation.","intents":["I want to quickly understand why this incident happened","I need suggestions on how to fix this issue","I want to reduce the time spent investigating incidents"],"best_for":["teams managing complex distributed systems","organizations with limited on-call expertise","teams wanting to accelerate incident resolution"],"limitations":["accuracy depends on quality of incident metadata and logs","may not identify root causes in novel or unprecedented scenarios"],"requires":["incident history","system logs","metrics data","service topology information"],"input_types":["incident data","system logs","metrics","service dependencies"],"output_types":["root cause hypothesis","remediation recommendations","severity assessment"],"categories":["reliability","incident management","AI"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_10","uri":"capability://observability.alert.rule.recommendation.and.tuning","name":"alert rule recommendation and tuning","description":"Analyzes alert history and incident data to recommend new alert rules or suggest tuning of existing ones. Helps teams find the right balance between coverage and noise.","intents":["I want suggestions on what alerts I should be monitoring","I need to tune my alert thresholds to reduce false positives","I want to identify gaps in my monitoring coverage"],"best_for":["teams building or refining monitoring strategies","organizations with immature alerting","teams wanting data-driven alert configuration"],"limitations":["recommendations are based on historical data","may not account for business context or SLO requirements"],"requires":["alert history","incident data","metrics data"],"input_types":["alert records","incident records","metrics"],"output_types":["alert recommendations","threshold suggestions","coverage analysis"],"categories":["observability","reliability","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_11","uri":"capability://incident.management.incident.post.mortem.and.learning.generation","name":"incident post-mortem and learning generation","description":"Automatically generates post-mortem summaries and learning documents from incident data. Extracts key insights, timeline, impact, and recommendations for preventing similar incidents.","intents":["I want to quickly document what happened in an incident","I need to capture learnings to prevent similar incidents","I want to reduce the time spent writing post-mortems"],"best_for":["organizations practicing blameless post-mortems","teams wanting to capture institutional knowledge","enterprises with compliance requirements"],"limitations":["generated content requires human review and editing","may miss nuanced learnings that require human judgment"],"requires":["incident data","timeline information","resolution details"],"input_types":["incident records","timeline data","resolution steps","team feedback"],"output_types":["post-mortem documents","learning summaries","prevention recommendations"],"categories":["incident management","knowledge management","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_12","uri":"capability://observability.observability.data.aggregation.and.normalization","name":"observability data aggregation and normalization","description":"Aggregates and normalizes observability data from multiple sources (logs, metrics, traces, events) into a unified format for analysis and correlation. Handles different data formats and sources transparently.","intents":["I want to correlate data from my different monitoring tools","I need a unified view of my system health across all data sources","I want to avoid vendor lock-in by working with multiple observability platforms"],"best_for":["organizations using multiple observability platforms","teams with heterogeneous monitoring stacks","enterprises wanting flexibility in tool choices"],"limitations":["requires integration with each data source","data normalization may lose source-specific details"],"requires":["access to multiple observability platforms","API credentials","data export permissions"],"input_types":["logs","metrics","traces","events","custom data"],"output_types":["normalized data","unified records","correlated datasets"],"categories":["observability","integration","data management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_13","uri":"capability://analytics.team.performance.analytics.and.insights","name":"team performance analytics and insights","description":"Analyzes team performance metrics including incident response times, resolution rates, on-call load, and skill distribution. Provides insights into team health and identifies areas for improvement.","intents":["I want to understand how well my team is performing","I need to identify skill gaps or training needs","I want to track team productivity and efficiency over time"],"best_for":["engineering managers","organizations focused on team development","teams wanting data-driven management"],"limitations":["metrics may not capture all aspects of team performance","requires careful interpretation to avoid misuse"],"requires":["incident data","team membership data","time tracking data"],"input_types":["incident records","team data","resolution metrics"],"output_types":["performance dashboards","trend analysis","insights and recommendations"],"categories":["analytics","team management","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_2","uri":"capability://reliability.intelligent.alert.filtering.and.noise.reduction","name":"intelligent alert filtering and noise reduction","description":"Learns which alerts are actionable versus noise and automatically suppresses or deprioritizes low-signal alerts. Adapts over time based on team behavior and incident outcomes.","intents":["I want to focus only on alerts that actually matter","I need to reduce alert fatigue without missing critical issues","I want the system to learn what's important to my team"],"best_for":["teams with mature monitoring but poor signal-to-noise ratio","organizations with diverse alert sources","teams with established incident response patterns"],"limitations":["requires time to learn team patterns","may suppress legitimate alerts if not properly tuned"],"requires":["alert history","incident response data","team feedback"],"input_types":["alert streams","alert metadata","incident resolution data"],"output_types":["filtered alert queue","prioritized alerts","suppression rules"],"categories":["reliability","incident management","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_3","uri":"capability://integration.pagerduty.and.incident.platform.integration","name":"pagerduty and incident platform integration","description":"Seamlessly connects with PagerDuty and other incident management platforms to enrich incidents with AI insights, automate escalations, and synchronize incident state across systems.","intents":["I want my incident management tool to have AI-powered insights","I need to automate incident routing and escalation decisions","I want to keep my existing incident management workflow but add AI capabilities"],"best_for":["organizations already using PagerDuty","teams wanting to enhance existing incident workflows","enterprises with standardized incident management"],"limitations":["limited to platforms with available integrations","requires proper API permissions and authentication"],"requires":["PagerDuty account or compatible incident platform","API credentials","proper permissions"],"input_types":["incident events","incident metadata","escalation policies"],"output_types":["enriched incidents","automated escalations","incident assignments"],"categories":["integration","incident management","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_4","uri":"capability://integration.slack.and.teams.notification.and.collaboration","name":"slack and teams notification and collaboration","description":"Sends intelligent incident notifications and summaries to Slack and Microsoft Teams, enabling teams to collaborate on incidents without leaving their communication platform. Supports interactive incident management directly in chat.","intents":["I want incident updates in Slack/Teams where my team already communicates","I need to collaborate on incident response without switching tools","I want to acknowledge and manage incidents from chat"],"best_for":["teams using Slack or Teams as primary communication","distributed teams","organizations wanting to reduce context switching"],"limitations":["limited to Slack and Teams","chat-based interactions may not suit complex incident workflows"],"requires":["Slack or Teams workspace","bot permissions","channel configuration"],"input_types":["incident events","incident updates","user actions"],"output_types":["formatted messages","interactive buttons","incident summaries"],"categories":["integration","collaboration","incident management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_5","uri":"capability://observability.service.dependency.mapping.and.visualization","name":"service dependency mapping and visualization","description":"Automatically discovers and visualizes service dependencies and relationships within a system architecture. Helps teams understand system topology and how incidents propagate across services.","intents":["I want to understand how my services depend on each other","I need to see how an incident in one service affects others","I want to visualize my system architecture automatically"],"best_for":["organizations with microservices architectures","teams managing complex distributed systems","teams lacking clear documentation of service dependencies"],"limitations":["accuracy depends on observability instrumentation","may not capture all indirect dependencies"],"requires":["instrumentation data","tracing data","service metadata"],"input_types":["traces","metrics","service metadata","API calls"],"output_types":["dependency graph","service topology visualization","impact analysis"],"categories":["observability","reliability","visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_6","uri":"capability://observability.incident.timeline.reconstruction.and.context.enrichment","name":"incident timeline reconstruction and context enrichment","description":"Automatically reconstructs incident timelines by correlating events from multiple sources (logs, metrics, traces, events) and enriches them with relevant context like deployments, configuration changes, and system state.","intents":["I want to understand the sequence of events that led to this incident","I need to see what changed in the system around the time of the incident","I want a complete picture of what happened without manual log searching"],"best_for":["teams investigating complex incidents","organizations with multiple data sources","teams wanting faster incident understanding"],"limitations":["requires comprehensive instrumentation","clock skew across systems can affect accuracy"],"requires":["logs","metrics","traces","event data","deployment records"],"input_types":["logs","metrics","traces","events","deployment data"],"output_types":["incident timeline","enriched events","context summaries"],"categories":["observability","incident management","reliability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_7","uri":"capability://analytics.mean.time.to.resolution.mttr.tracking.and.optimization","name":"mean time to resolution (mttr) tracking and optimization","description":"Tracks MTTR metrics across incidents and provides insights into bottlenecks and optimization opportunities. Identifies patterns in what makes some teams resolve incidents faster than others.","intents":["I want to measure how fast my team resolves incidents","I need to identify what's slowing down our incident response","I want to benchmark our MTTR against industry standards"],"best_for":["organizations focused on reliability metrics","teams wanting to improve incident response efficiency","enterprises tracking SLOs"],"limitations":["MTTR is influenced by many factors beyond tooling","requires consistent incident data collection"],"requires":["incident history","incident resolution timestamps","incident metadata"],"input_types":["incident records","resolution times","incident classifications"],"output_types":["MTTR metrics","trend analysis","optimization recommendations"],"categories":["analytics","reliability","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_8","uri":"capability://incident.management.on.call.schedule.management.and.optimization","name":"on-call schedule management and optimization","description":"Manages on-call rotations and suggests optimizations based on incident patterns, team capacity, and historical performance. Helps balance on-call load fairly across teams.","intents":["I want to manage who is on-call and when","I need to ensure fair distribution of on-call burden","I want to optimize on-call schedules based on incident patterns"],"best_for":["organizations with formal on-call programs","teams wanting to reduce on-call burnout","enterprises managing multiple teams"],"limitations":["requires integration with scheduling systems","optimization suggestions may not account for all team preferences"],"requires":["on-call schedule data","incident history","team information"],"input_types":["schedule data","incident records","team preferences"],"output_types":["schedule recommendations","load analysis","fairness metrics"],"categories":["incident management","productivity","team management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rely-io__cap_9","uri":"capability://automation.runbook.and.playbook.automation","name":"runbook and playbook automation","description":"Automatically executes remediation runbooks and playbooks in response to detected incidents. Can trigger scripts, API calls, and remediation actions without manual intervention.","intents":["I want to automatically fix common incidents without human intervention","I need to execute remediation steps consistently and quickly","I want to reduce manual toil in incident response"],"best_for":["teams with well-defined remediation procedures","organizations wanting to reduce MTTR","teams with repetitive incident patterns"],"limitations":["requires pre-defined runbooks","may not be suitable for novel or complex incidents","requires proper access controls and safety measures"],"requires":["runbook definitions","API access","proper permissions","safety guardrails"],"input_types":["incident data","runbook triggers","incident context"],"output_types":["remediation actions","execution logs","action results"],"categories":["automation","incident management","reliability"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["integration with monitoring/alerting systems","incident history data","incident history","system logs","metrics data","service topology information","alert history","incident data","timeline information","resolution details"],"failure_modes":["requires sufficient historical incident data to train correlation models","may miss novel incident patterns not seen before","accuracy depends on quality of incident metadata and logs","may not identify root causes in novel or unprecedented scenarios","recommendations are based on historical data","may not account for business context or SLO requirements","generated content requires human review and editing","may miss nuanced learnings that require human judgment","requires integration with each data source","data normalization may lose source-specific details","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.41666666666666663,"quality":0.84,"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":"2026-05-24T12:16:33.094Z","last_scraped_at":"2026-04-05T13:23:42.537Z","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=rely-io","compare_url":"https://unfragile.ai/compare?artifact=rely-io"}},"signature":"fOjyTfWYtZfKDo9ku/LTZ/PnnWuFTQcP+zf6hNVKcpuJGawPdm/hIHM3uLm5dctaV1r81yCu5rz3nXby4JkQDQ==","signedAt":"2026-06-23T00:57:02.176Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rely-io","artifact":"https://unfragile.ai/rely-io","verify":"https://unfragile.ai/api/v1/verify?slug=rely-io","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"}}