{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_archetype-ai","slug":"archetype-ai","name":"Archetype AI","type":"product","url":"https://www.archetypeai.io","page_url":"https://unfragile.ai/archetype-ai","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_archetype-ai__cap_0","uri":"capability://data.processing.analysis.multi.sensor.fusion.and.contextual.data.aggregation","name":"multi-sensor fusion and contextual data aggregation","description":"Ingests heterogeneous sensor streams (temperature, humidity, pressure, motion, vibration, etc.) and applies machine learning-based fusion algorithms to correlate signals across multiple sensors, extracting contextual patterns that would be invisible in siloed analysis. The system normalizes disparate sensor protocols and sampling rates into a unified temporal framework, enabling cross-domain pattern recognition rather than treating each sensor independently.","intents":["I need to understand what's really happening in my facility by correlating signals from dozens of different sensors instead of monitoring each one separately","I want to detect anomalies that only emerge when multiple sensors deviate together, not just individual threshold breaches","I need to fuse sensor data from different manufacturers and protocols into a single coherent view"],"best_for":["manufacturing operations with distributed sensor networks across production lines","smart building operators managing HVAC, occupancy, and energy systems simultaneously","logistics companies tracking environmental conditions (temperature, humidity, shock) across shipments"],"limitations":["Requires pre-existing sensor infrastructure — cannot generate insights from facilities with sparse or legacy sensor deployments","Fusion accuracy degrades with high sensor dropout rates or inconsistent sampling intervals","No built-in sensor calibration — assumes input sensors are already validated and calibrated"],"requires":["Active sensor deployment (minimum 3+ correlated sensors for meaningful fusion)","Network connectivity to stream sensor data (MQTT, HTTP, or proprietary IoT protocols)","Historical sensor data (typically 2-4 weeks minimum) for model training and baseline establishment"],"input_types":["time-series sensor streams (numeric values with timestamps)","sensor metadata (type, location, units, sampling frequency)","historical sensor logs (CSV, Parquet, or streaming APIs)"],"output_types":["structured anomaly alerts with confidence scores","contextual event descriptions (e.g., 'HVAC malfunction detected: temperature rising while humidity drops unexpectedly')","correlation matrices showing sensor interdependencies"],"categories":["data-processing-analysis","iot-analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_archetype-ai__cap_1","uri":"capability://data.processing.analysis.real.time.anomaly.detection.with.streaming.inference","name":"real-time anomaly detection with streaming inference","description":"Processes incoming sensor data streams with sub-second latency using pre-trained ML models deployed at the edge or cloud, detecting deviations from learned normal behavior patterns. The system maintains a rolling baseline of expected sensor behavior and flags statistical outliers, sudden shifts, or pattern breaks as anomalies, with configurable sensitivity thresholds and suppression of cascading false positives from correlated sensors.","intents":["I need to catch equipment failures or environmental issues the moment they start, not hours later when I review logs","I want to reduce false alarms from transient sensor noise while still catching real problems","I need anomaly detection that adapts to seasonal or operational changes without manual recalibration"],"best_for":["facility managers requiring immediate alerts for critical conditions (e.g., temperature excursions in cold storage)","predictive maintenance teams wanting early warning before catastrophic failures","compliance-heavy industries (pharma, food) needing audit trails of detected anomalies"],"limitations":["Requires 2-4 weeks of baseline data to establish normal behavior — cannot detect anomalies in cold-start scenarios","Sensitivity tuning is manual and domain-specific; no automated threshold optimization","Struggles with gradual drift (e.g., sensor degradation over months) vs. sudden faults","No built-in causal analysis — detects that something is wrong but not why"],"requires":["Continuous sensor data stream (minimum 1 Hz sampling for most industrial applications)","Network latency < 5 seconds for real-time alerting use cases","Historical baseline data (2-4 weeks minimum) for model training"],"input_types":["time-series numeric data (sensor readings with timestamps)","optional: categorical metadata (equipment type, operational mode, shift)"],"output_types":["anomaly alerts with timestamp, sensor ID, and deviation magnitude","confidence scores (0-1) indicating likelihood of true anomaly vs. noise","contextual metadata (e.g., which other sensors also deviated, historical frequency of similar events)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_archetype-ai__cap_2","uri":"capability://planning.reasoning.predictive.maintenance.scoring.with.failure.risk.quantification","name":"predictive maintenance scoring with failure risk quantification","description":"Analyzes historical sensor patterns and equipment failure events to train models that predict the probability and estimated time-to-failure for assets. The system ingests maintenance logs, failure records, and sensor data to learn which sensor signatures precede failures, then scores current equipment health on a continuous risk scale (0-100) with projected failure windows. Incorporates remaining useful life (RUL) estimation using degradation curves learned from historical data.","intents":["I want to schedule maintenance before equipment fails, not after it breaks and causes downtime","I need to prioritize maintenance work across hundreds of assets based on actual failure risk, not just age or run-time","I want to optimize spare parts inventory by knowing which equipment is likely to fail in the next 30/60/90 days"],"best_for":["manufacturing plants with high-value equipment where unplanned downtime is costly","logistics operations managing fleets of vehicles or material handling equipment","facility managers responsible for HVAC, electrical, or mechanical systems across multiple buildings"],"limitations":["Requires historical failure data (minimum 20-50 failure events per asset type) — limited effectiveness for new equipment with no failure history","Accuracy degrades for rare failure modes or novel equipment configurations not seen in training data","Does not account for external factors (operator skill, maintenance quality, environmental stress) unless explicitly provided as features","RUL estimates are probabilistic; cannot guarantee failure will occur within predicted window"],"requires":["Historical maintenance logs with failure dates and root causes","Sensor data preceding failures (minimum 3-6 months of continuous data before each failure event)","Equipment metadata (model, age, operating hours, maintenance history)","Minimum 20-50 historical failure events per asset type for reliable model training"],"input_types":["time-series sensor data (temperature, vibration, pressure, etc.)","structured maintenance records (failure date, root cause, repair type, downtime duration)","equipment metadata (asset ID, type, age, location, operational parameters)"],"output_types":["risk scores (0-100) for each asset, updated continuously","estimated time-to-failure (days/weeks) with confidence intervals","failure probability curves (e.g., 30% chance of failure in next 30 days)","contributing factors (which sensors or conditions are driving high risk scores)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_archetype-ai__cap_3","uri":"capability://text.generation.language.natural.language.insight.generation.and.report.synthesis","name":"natural language insight generation and report synthesis","description":"Transforms raw sensor data, anomalies, and predictive scores into human-readable narratives and structured reports using natural language generation. The system contextualizes technical findings (e.g., 'vibration increased 40%') into business-relevant insights (e.g., 'bearing degradation detected; recommend replacement within 2 weeks to avoid unplanned downtime'). Generates executive summaries, detailed technical reports, and actionable recommendations tailored to different stakeholder roles (operators, maintenance managers, facility directors).","intents":["I need to explain complex sensor data findings to non-technical stakeholders without overwhelming them with numbers","I want automated reports that summarize what happened, why it matters, and what to do about it","I need to communicate maintenance recommendations in a way that justifies the cost and urgency to budget holders"],"best_for":["facility managers and operations directors who need executive summaries for decision-making","maintenance teams requiring detailed technical reports with actionable next steps","compliance officers needing audit trails and documented findings for regulatory purposes"],"limitations":["Limited transparency on how recommendations are generated — difficult to validate in regulated industries requiring explainability","NLG output quality depends on training data; may produce generic or contextually inappropriate language for niche industries","Cannot incorporate domain-specific jargon or company-specific terminology without customization","No built-in fact-checking — may generate plausible-sounding but technically incorrect narratives if underlying data is ambiguous"],"requires":["Structured input data (anomalies, risk scores, sensor readings with clear semantics)","Optional: domain-specific terminology or company style guides for customized language generation"],"input_types":["structured anomaly data (sensor ID, deviation magnitude, timestamp, confidence score)","predictive scores (risk level, estimated time-to-failure, contributing factors)","historical context (previous similar events, maintenance history, operational parameters)"],"output_types":["natural language summaries (1-3 paragraphs explaining findings and implications)","structured reports (JSON or PDF with sections: executive summary, technical findings, recommendations, risk assessment)","role-specific narratives (operator-friendly alerts, maintenance-focused technical reports, executive dashboards)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_archetype-ai__cap_4","uri":"capability://data.processing.analysis.multi.protocol.sensor.data.ingestion.and.normalization","name":"multi-protocol sensor data ingestion and normalization","description":"Accepts sensor data from diverse sources (MQTT brokers, HTTP APIs, Modbus, OPC-UA, proprietary IoT platforms) and normalizes heterogeneous data formats into a unified schema. The system handles protocol translation, timestamp synchronization across sensors with different clock sources, unit conversion (e.g., Celsius to Fahrenheit), and data quality validation (detecting missing values, out-of-range readings, duplicate timestamps). Supports both real-time streaming and batch historical data imports.","intents":["I have sensors from 5 different manufacturers using different protocols and I need to analyze them together","I need to ingest historical sensor data from legacy systems and new IoT platforms without building custom ETL pipelines","I want to ensure data quality and consistency before it reaches the analytics engine"],"best_for":["organizations with heterogeneous sensor ecosystems (legacy + modern IoT)","facility managers integrating data from multiple building management systems","industrial operations with sensors from different equipment vendors"],"limitations":["Requires configuration for each new sensor type/protocol — no fully automatic discovery","Protocol support is fixed; adding new protocols requires platform updates","Data quality validation is rule-based; cannot detect semantic errors (e.g., sensor reading physically impossible but within stated range)","Timestamp synchronization assumes network latency < 5 seconds; struggles with high-latency or intermittent connections"],"requires":["Network access to sensor sources (MQTT broker, HTTP endpoint, Modbus gateway, etc.)","Sensor metadata (protocol type, data format, units, sampling frequency, expected range)","Optional: authentication credentials for secured sensor endpoints"],"input_types":["MQTT messages (JSON or binary payloads)","HTTP/REST API responses (JSON, XML, or CSV)","Modbus registers (coils, discrete inputs, holding registers)","OPC-UA node values","CSV/Parquet files (batch historical imports)","Proprietary IoT platform APIs (AWS IoT, Azure IoT Hub, Google Cloud IoT)"],"output_types":["normalized time-series data (standardized schema with timestamp, sensor ID, value, unit, quality flags)","data quality reports (missing values, outliers, synchronization errors)","ingestion logs (success/failure counts, latency metrics, data volume)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_archetype-ai__cap_5","uri":"capability://automation.workflow.contextual.alerting.with.suppression.and.escalation.rules","name":"contextual alerting with suppression and escalation rules","description":"Routes detected anomalies and risk events through a rule engine that suppresses false positives, correlates related alerts, and escalates based on severity, duration, and business context. The system can suppress alerts during known maintenance windows, combine multiple related sensor anomalies into a single incident, and escalate alerts to different teams (e.g., shift operators → maintenance manager → facility director) based on severity thresholds and time-of-day. Supports custom notification channels (email, SMS, Slack, PagerDuty) and acknowledgment workflows.","intents":["I'm drowning in false alerts and need to filter noise while still catching real problems","I want related sensor anomalies grouped into a single incident instead of 20 separate alerts","I need alerts to go to the right person at the right time (e.g., escalate to manager if operator doesn't acknowledge within 15 minutes)"],"best_for":["operations centers managing 24/7 facilities with multiple shifts and on-call schedules","maintenance teams needing to prioritize work based on alert severity and business impact","organizations with compliance requirements for documented incident response and escalation"],"limitations":["Rule configuration is manual and domain-specific — no automatic learning of optimal suppression rules","Escalation logic is static; cannot adapt to dynamic staffing or availability","No built-in integration with external incident management systems (Jira, ServiceNow) — requires custom webhooks","Alert correlation is rule-based; cannot detect novel multi-sensor incident patterns"],"requires":["Configured notification channels (email, SMS, Slack, PagerDuty, etc.)","Escalation policy definition (who to alert, when, and via which channel)","Optional: integration with on-call scheduling systems for dynamic escalation"],"input_types":["anomaly alerts (sensor ID, deviation magnitude, timestamp, confidence score)","risk scores (asset health, failure probability)","operational context (maintenance windows, shift schedules, equipment status)"],"output_types":["filtered/correlated alerts (grouped incidents with consolidated severity)","notification messages (formatted for specific channels: email, SMS, Slack, etc.)","escalation logs (who was alerted, when, via which channel, acknowledgment status)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_archetype-ai__cap_6","uri":"capability://data.processing.analysis.asset.health.dashboards.with.drill.down.analytics","name":"asset health dashboards with drill-down analytics","description":"Provides interactive visualizations of equipment health, sensor trends, and predictive scores with drill-down capabilities from facility-level summaries to individual asset details. Dashboards display real-time sensor data, historical trends, anomaly timelines, and risk scores with configurable time windows and filtering. Supports custom dashboard creation for different stakeholder roles (operators, maintenance managers, executives) with role-based access control and data visibility restrictions.","intents":["I need a single pane of glass showing the health of all my equipment at a glance","I want to drill down from a high-level facility summary to understand what's happening with a specific asset","I need different views for different roles (operators see operational status, managers see maintenance priorities)"],"best_for":["facility operators and shift supervisors monitoring equipment status in real-time","maintenance managers prioritizing work and tracking asset health trends","executives reviewing facility performance and ROI from IoT investments"],"limitations":["Dashboard performance degrades with very large datasets (>1M data points per asset) — may require data aggregation or sampling","Customization options are limited to pre-built dashboard templates; no arbitrary visualization types","Real-time updates require WebSocket connections; may not work reliably over high-latency or unstable networks","No built-in export to external BI tools (Tableau, Power BI) — limited integration with existing analytics stacks"],"requires":["Web browser with modern JavaScript support (Chrome, Firefox, Safari, Edge)","Network connectivity to Archetype AI platform (HTTPS, WebSocket)","User authentication (API key or SSO integration)"],"input_types":["real-time sensor data streams","historical time-series data","anomaly and risk scores","equipment metadata and operational context"],"output_types":["interactive web dashboards (real-time and historical views)","drill-down analytics (facility → building → equipment → sensor level)","exportable reports (PDF, CSV) with selected time windows and metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_archetype-ai__cap_7","uri":"capability://planning.reasoning.model.explainability.and.feature.importance.analysis","name":"model explainability and feature importance analysis","description":"Provides transparency into which sensor readings and features most strongly influence anomaly detection and failure risk predictions. The system generates feature importance scores showing which sensors or combinations of sensors drive each prediction, and produces counterfactual explanations (e.g., 'if vibration were 10% lower, risk score would drop from 75 to 45'). Supports SHAP values, permutation importance, and attention-based explanations depending on the underlying model architecture.","intents":["I need to understand why the system flagged this equipment as high-risk so I can validate the recommendation","I want to know which sensors are most important for predicting failures so I can prioritize sensor maintenance","I need to explain model decisions to auditors and regulators in compliance-heavy industries"],"best_for":["regulated industries (pharma, food, aerospace) requiring documented decision rationale","organizations with domain expertise wanting to validate model recommendations against their knowledge","data scientists and engineers optimizing sensor deployments based on feature importance"],"limitations":["Explainability is limited to post-hoc analysis; cannot guarantee model decisions align with human intuition","Feature importance scores are model-specific and may not be comparable across different model versions","Counterfactual explanations assume linear relationships; may be misleading for highly non-linear models","Generating detailed explanations adds latency (typically 100-500ms per prediction) — not suitable for real-time alerting"],"requires":["Access to trained model internals (weights, architecture, training data statistics)","Sufficient historical data to compute reliable feature importance scores"],"input_types":["trained ML models (neural networks, gradient boosting, etc.)","sensor data and features used in predictions","historical prediction outcomes and ground truth labels"],"output_types":["feature importance scores (0-1 scale indicating relative influence)","SHAP values (per-prediction contribution of each feature)","counterfactual explanations (how much each feature would need to change to alter the prediction)","decision trees or rule sets approximating model behavior"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Active sensor deployment (minimum 3+ correlated sensors for meaningful fusion)","Network connectivity to stream sensor data (MQTT, HTTP, or proprietary IoT protocols)","Historical sensor data (typically 2-4 weeks minimum) for model training and baseline establishment","Continuous sensor data stream (minimum 1 Hz sampling for most industrial applications)","Network latency < 5 seconds for real-time alerting use cases","Historical baseline data (2-4 weeks minimum) for model training","Historical maintenance logs with failure dates and root causes","Sensor data preceding failures (minimum 3-6 months of continuous data before each failure event)","Equipment metadata (model, age, operating hours, maintenance history)","Minimum 20-50 historical failure events per asset type for reliable model training"],"failure_modes":["Requires pre-existing sensor infrastructure — cannot generate insights from facilities with sparse or legacy sensor deployments","Fusion accuracy degrades with high sensor dropout rates or inconsistent sampling intervals","No built-in sensor calibration — assumes input sensors are already validated and calibrated","Requires 2-4 weeks of baseline data to establish normal behavior — cannot detect anomalies in cold-start scenarios","Sensitivity tuning is manual and domain-specific; no automated threshold optimization","Struggles with gradual drift (e.g., sensor degradation over months) vs. sudden faults","No built-in causal analysis — detects that something is wrong but not why","Requires historical failure data (minimum 20-50 failure events per asset type) — limited effectiveness for new equipment with no failure history","Accuracy degrades for rare failure modes or novel equipment configurations not seen in training data","Does not account for external factors (operator skill, maintenance quality, environmental stress) unless explicitly provided as features","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"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":"2026-05-24T12:16:29.133Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=archetype-ai","compare_url":"https://unfragile.ai/compare?artifact=archetype-ai"}},"signature":"JQt7aLVkvBIlBEMGYpT8V/g7AiIzTb0JuxShQ1TXrA534rCRQXzqo+qnrpRIZlVKH/6eT5uj6njaRE/4Ly8CBA==","signedAt":"2026-06-20T23:50:15.970Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/archetype-ai","artifact":"https://unfragile.ai/archetype-ai","verify":"https://unfragile.ai/api/v1/verify?slug=archetype-ai","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"}}