{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_patterned-ai","slug":"patterned-ai","name":"Patterned AI","type":"product","url":"http://patterned.ai","page_url":"https://unfragile.ai/patterned-ai","categories":["data-analysis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_patterned-ai__cap_0","uri":"capability://data.processing.analysis.unsupervised.pattern.detection.in.tabular.datasets","name":"unsupervised pattern detection in tabular datasets","description":"Automatically identifies recurring patterns, clusters, and anomalies in structured data without requiring labeled training data or manual feature engineering. Uses machine learning algorithms (likely clustering, dimensionality reduction, or statistical anomaly detection) to surface hidden relationships across multiple dimensions simultaneously, then ranks patterns by statistical significance and actionability for design decision-making.","intents":["I need to find hidden behavioral clusters in user interaction data to segment design personas","I want to detect outlier patterns in user feedback that might indicate unmet needs","I need to identify correlations between user attributes and design preferences without manually exploring every combination"],"best_for":["UX researchers and designers analyzing user behavior datasets","Product teams identifying user segments for targeted design iterations","Design-focused teams without data science expertise"],"limitations":["Pattern detection quality depends on dataset size and dimensionality — small datasets (<100 rows) may produce spurious patterns","No control over algorithm selection or hyperparameters on free tier — black-box approach limits interpretability","Requires clean, structured input data; unhandled missing values or categorical encoding may degrade pattern quality"],"requires":["Structured dataset (CSV, JSON, or platform upload format)","Minimum ~50 data points for meaningful pattern detection","Active Patterned AI account (freemium tier available)"],"input_types":["CSV files","JSON structured data","platform-native data uploads"],"output_types":["pattern clusters with statistical confidence scores","anomaly flags with deviation magnitude","pattern metadata (size, prevalence, key distinguishing attributes)"],"categories":["data-processing-analysis","pattern-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_patterned-ai__cap_1","uri":"capability://image.visual.interactive.pattern.visualization.with.design.centric.layouts","name":"interactive pattern visualization with design-centric layouts","description":"Transforms detected patterns into interactive visual representations (likely scatter plots, heatmaps, network graphs, or parallel coordinates) optimized for design decision-making rather than statistical reporting. Visualization engine allows filtering, drilling down into pattern subsets, and comparing pattern characteristics side-by-side to extract actionable design insights.","intents":["I need to visualize user behavior clusters in a way that helps me communicate insights to non-technical stakeholders","I want to explore how different user attributes correlate with design preferences interactively","I need to identify which pattern characteristics are most relevant to my design problem"],"best_for":["Design teams presenting pattern insights to product and leadership stakeholders","Individual designers exploring data interactively to inform creative direction","Teams needing to communicate data-driven design rationale"],"limitations":["Visualization types are pre-defined by platform — no custom chart types or D3.js-level customization","Export to image/PDF limited on free tier; high-resolution exports require paid plan","Performance degrades with >10k data points; large datasets may require sampling or aggregation"],"requires":["Completed pattern detection on a dataset","Modern web browser with WebGL support for interactive rendering","Patterned AI account with visualization access"],"input_types":["pattern detection output","user-selected pattern subsets"],"output_types":["interactive HTML visualizations","static image exports (PNG, SVG on paid tier)","pattern summary reports"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_patterned-ai__cap_2","uri":"capability://data.processing.analysis.behavioral.pattern.extraction.for.persona.and.segment.definition","name":"behavioral pattern extraction for persona and segment definition","description":"Automatically synthesizes detected patterns into actionable persona definitions and user segment descriptions by identifying common behavioral traits, preferences, and characteristics within each cluster. Generates natural language summaries of each pattern (e.g., 'power users who prioritize speed over customization') and maps patterns to design implications, enabling designers to move directly from data to persona-informed design decisions.","intents":["I need to convert raw user behavior clusters into design personas I can use in my design process","I want to understand what design decisions would resonate with each user segment","I need to generate persona descriptions and behavioral profiles from data without manual synthesis"],"best_for":["UX designers creating data-driven personas for design systems","Product teams defining segment-specific design strategies","Teams moving from assumption-based to data-driven persona development"],"limitations":["Persona synthesis is automated — may miss nuanced behavioral context that manual analysis would capture","Natural language summaries are template-based; customization of persona narrative is limited on free tier","Requires sufficient pattern distinctiveness; overlapping clusters may produce redundant or confusing persona definitions"],"requires":["Completed pattern detection with at least 3-5 distinct clusters","Dataset with behavioral or preference attributes (not purely demographic)","Patterned AI account"],"input_types":["pattern clusters from detection engine","user-provided pattern labels or context"],"output_types":["persona definitions with behavioral traits","segment size and prevalence metrics","design implication summaries","persona comparison matrices"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_patterned-ai__cap_3","uri":"capability://data.processing.analysis.trend.and.temporal.pattern.detection.across.time.series.data","name":"trend and temporal pattern detection across time-series data","description":"Identifies evolving patterns and trends in time-series or sequential data by analyzing how user behaviors, preferences, or characteristics change over time periods. Detects trend acceleration, seasonal cycles, and inflection points that signal shifts in user needs or design preferences, enabling designers to anticipate future design requirements and identify windows for design iteration.","intents":["I need to identify when user behavior or preferences are shifting to time my design changes","I want to detect seasonal or cyclical patterns in user engagement to inform feature prioritization","I need to find inflection points where user needs change significantly"],"best_for":["Product designers planning design roadmaps based on user trend data","Teams analyzing user engagement trends to inform design refresh cycles","Designers needing to anticipate future user needs from historical behavior"],"limitations":["Requires time-stamped data with sufficient temporal granularity — daily or hourly data needed for meaningful trend detection","Trend detection accuracy improves with longer historical periods (minimum 3-6 months of data recommended)","Seasonal pattern detection requires at least 2 full cycles of the suspected season to be reliable"],"requires":["Time-series dataset with timestamp and behavioral/preference metrics","Minimum 50-100 time periods (days/weeks) of historical data","Patterned AI account with time-series analysis enabled"],"input_types":["time-stamped CSV or JSON data","user engagement metrics with dates","behavioral event logs"],"output_types":["trend direction and acceleration metrics","seasonal cycle identification with period length","inflection point timestamps and magnitude","forecast confidence intervals"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_patterned-ai__cap_4","uri":"capability://data.processing.analysis.cross.dataset.pattern.correlation.and.comparison","name":"cross-dataset pattern correlation and comparison","description":"Enables comparison of patterns detected across multiple datasets or time periods to identify correlations between user segments and design outcomes, or to track how patterns evolve across product versions. Uses statistical correlation analysis to determine whether pattern characteristics in one dataset predict or correlate with outcomes in another, supporting hypothesis testing and design validation.","intents":["I need to correlate user behavior patterns with design performance metrics to validate design decisions","I want to compare user segments across different product versions to measure design impact","I need to test whether a specific user behavior pattern predicts positive design outcomes"],"best_for":["Design teams validating design decisions with behavioral data","Product managers measuring design impact on user segments","Teams running A/B tests and needing to correlate user patterns with outcomes"],"limitations":["Correlation analysis requires aligned datasets with comparable dimensions — mismatched schemas require manual mapping","Statistical significance thresholds are platform-determined; no customization of confidence levels on free tier","Causality cannot be inferred from correlation — platform provides correlation strength but not causal mechanisms"],"requires":["Two or more datasets with overlapping or comparable dimensions","Minimum 50 data points per dataset for meaningful correlation","Patterned AI account with multi-dataset analysis enabled"],"input_types":["multiple pattern detection outputs","user behavior datasets with design outcome metrics","A/B test data with control and variant segments"],"output_types":["correlation matrices between pattern characteristics and outcomes","statistical significance scores","pattern-outcome relationship visualizations","hypothesis validation reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_patterned-ai__cap_5","uri":"capability://planning.reasoning.pattern.to.design.recommendation.synthesis","name":"pattern-to-design-recommendation synthesis","description":"Automatically generates design recommendations based on detected patterns by mapping pattern characteristics to design principles, interaction patterns, and feature priorities. Uses pattern metadata (size, distinctiveness, behavioral traits) to suggest design changes, feature prioritization, and interaction design approaches tailored to each user segment, bridging the gap between data insights and actionable design decisions.","intents":["I need design recommendations based on user pattern data without manually interpreting statistics","I want to prioritize features based on which user segments would benefit most","I need to understand what interaction design approaches would work best for each user segment"],"best_for":["Designers seeking data-driven design direction without data science expertise","Product teams translating user patterns into feature prioritization","Teams needing to justify design decisions with pattern-based rationale"],"limitations":["Recommendations are template-based and may not account for domain-specific design constraints or brand guidelines","Recommendation confidence varies by pattern clarity — ambiguous patterns produce lower-confidence recommendations","No integration with design systems or component libraries — recommendations are conceptual, not implementation-ready"],"requires":["Completed pattern detection with clear, distinct clusters","Pattern metadata including segment size and behavioral characteristics","Patterned AI account"],"input_types":["pattern detection output with behavioral traits","user segment definitions","optional: current design or feature list for context"],"output_types":["design recommendation summaries per segment","feature prioritization rankings by segment","interaction design approach suggestions","design rationale documentation"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_patterned-ai__cap_6","uri":"capability://tool.use.integration.freemium.tier.pattern.detection.with.limited.export","name":"freemium-tier pattern detection with limited export","description":"Provides access to core pattern detection and visualization capabilities on a free tier with restricted export functionality — users can detect patterns, visualize them interactively, and view insights within the platform, but cannot export high-resolution visualizations, raw pattern data, or integrate with external design tools without upgrading to paid plans. Freemium model enables experimentation and validation before committing to paid features.","intents":["I want to experiment with pattern detection on my data before committing budget","I need to validate that pattern detection will be useful for my design process","I want to explore user data insights without paying for a full analytics platform"],"best_for":["Individual designers and small teams with limited budgets","Teams evaluating Patterned AI before enterprise adoption","Designers wanting to prototype data-driven design workflows"],"limitations":["Free tier export is limited to low-resolution PNG images — no vector exports, PDF reports, or data exports","No API access on free tier — cannot integrate pattern detection into automated workflows or design tools","Advanced pattern detection features (custom algorithms, hyperparameter tuning) restricted to paid tiers","Free tier may have rate limits or dataset size restrictions not documented in public materials"],"requires":["Patterned AI account (free signup)","No credit card required for free tier access"],"input_types":["CSV, JSON, or platform-native data uploads"],"output_types":["interactive web visualizations","low-resolution PNG exports","pattern summaries within platform"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_patterned-ai__cap_7","uri":"capability://tool.use.integration.design.tool.integration.and.export.paid.tier","name":"design-tool integration and export (paid tier)","description":"On paid tiers, enables export of pattern insights and visualizations to popular design tools (Figma, Adobe XD) and supports API-based integration for embedding pattern detection into design workflows. Allows designers to reference pattern-based personas, segment definitions, and design recommendations directly within design files, and enables automated pattern detection as part of design iteration cycles.","intents":["I need to reference user pattern insights directly in my Figma design files","I want to automate pattern detection as part of my design workflow","I need to share pattern-based design recommendations with my design team in our native tools"],"best_for":["Design teams using Figma or Adobe XD as primary design platforms","Teams wanting to embed pattern insights into design systems","Organizations with mature design workflows needing data integration"],"limitations":["Integration is limited to Figma and Adobe XD — no support for other design tools (Sketch, Penpot, etc.)","API access requires paid tier with potentially high cost for large-scale integration","Export formats are platform-specific — no generic data export for custom tool integration","Real-time sync between pattern updates and design files is not supported — exports are snapshots"],"requires":["Paid Patterned AI subscription (tier and pricing unknown from public materials)","Figma or Adobe XD account for design-tool integration","API credentials for programmatic access (if using API tier)"],"input_types":["pattern detection output","design file references (Figma/Adobe XD)"],"output_types":["Figma/Adobe XD plugin exports","API responses with pattern metadata","design recommendation documents"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"low","permissions":["Structured dataset (CSV, JSON, or platform upload format)","Minimum ~50 data points for meaningful pattern detection","Active Patterned AI account (freemium tier available)","Completed pattern detection on a dataset","Modern web browser with WebGL support for interactive rendering","Patterned AI account with visualization access","Completed pattern detection with at least 3-5 distinct clusters","Dataset with behavioral or preference attributes (not purely demographic)","Patterned AI account","Time-series dataset with timestamp and behavioral/preference metrics"],"failure_modes":["Pattern detection quality depends on dataset size and dimensionality — small datasets (<100 rows) may produce spurious patterns","No control over algorithm selection or hyperparameters on free tier — black-box approach limits interpretability","Requires clean, structured input data; unhandled missing values or categorical encoding may degrade pattern quality","Visualization types are pre-defined by platform — no custom chart types or D3.js-level customization","Export to image/PDF limited on free tier; high-resolution exports require paid plan","Performance degrades with >10k data points; large datasets may require sampling or aggregation","Persona synthesis is automated — may miss nuanced behavioral context that manual analysis would capture","Natural language summaries are template-based; customization of persona narrative is limited on free tier","Requires sufficient pattern distinctiveness; overlapping clusters may produce redundant or confusing persona definitions","Requires time-stamped data with sufficient temporal granularity — daily or hourly data needed for meaningful trend detection","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":"2026-05-24T12:16:32.437Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=patterned-ai","compare_url":"https://unfragile.ai/compare?artifact=patterned-ai"}},"signature":"QWJLW7WVyrETFQI0dlIfkez1yUzk71u5DDLmUGHK8kU1oneod3k1pAEGQPBg161V9AYkRIuLIP/i5A8gnaesDQ==","signedAt":"2026-06-22T17:42:21.794Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/patterned-ai","artifact":"https://unfragile.ai/patterned-ai","verify":"https://unfragile.ai/api/v1/verify?slug=patterned-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"}}