{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_quantplus","slug":"quantplus","name":"QuantPlus","type":"product","url":"https://www.quantplus.io","page_url":"https://unfragile.ai/quantplus","categories":["text-writing"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_quantplus__cap_0","uri":"capability://data.processing.analysis.performance.data.to.creative.direction.translation","name":"performance-data-to-creative-direction-translation","description":"Ingests structured performance metrics (CTR, conversion rates, engagement data, audience demographics) and applies machine learning inference to generate specific creative recommendations (copy angles, visual directions, messaging frameworks). The system likely uses supervised learning on historical campaign-to-creative mappings to identify patterns between performance outcomes and creative attributes, then outputs actionable creative briefs rather than raw analytics summaries.","intents":["I have 6 months of campaign performance data but don't know what creative changes to test next","I want to understand which creative elements (headlines, visuals, CTAs) correlate with my best-performing campaigns","I need to brief a designer or copywriter with data-backed creative direction instead of gut feel"],"best_for":["Marketing teams with historical campaign data but weak creative strategy processes","Agencies managing multiple client accounts who need to scale creative iteration","Solo marketers or small teams without dedicated creative staff"],"limitations":["Requires sufficient historical campaign data (likely minimum 20-50 campaigns) for meaningful pattern detection; sparse data yields generic recommendations","No transparency on which performance metrics are weighted in the analysis or how creative attributes are classified","Likely limited to specific ad platforms or data formats; integration with custom analytics systems unknown"],"requires":["Historical campaign performance data (CTR, conversion rate, spend, impressions minimum)","Access to QuantPlus platform (free tier available)","Campaign data in supported format (likely CSV, JSON, or native platform API)"],"input_types":["structured performance metrics (JSON/CSV)","campaign metadata (audience segments, platforms, date ranges)","creative asset descriptions or tags"],"output_types":["creative direction briefs (text)","structured recommendations (JSON with creative attributes and rationale)","priority-ranked creative hypotheses"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quantplus__cap_1","uri":"capability://data.processing.analysis.multi.campaign.pattern.recognition.and.clustering","name":"multi-campaign-pattern-recognition-and-clustering","description":"Analyzes performance data across multiple campaigns simultaneously to identify recurring patterns, successful audience segments, and creative themes that correlate with high performance. Uses unsupervised learning (clustering, dimensionality reduction) to group campaigns by outcome similarity and extract common attributes, enabling cross-campaign insights that single-campaign analysis cannot surface.","intents":["I want to find common traits across my 50 best-performing campaigns to identify a winning formula","I need to understand which audience segments consistently outperform others across different campaigns","I'm looking for creative patterns that work across multiple products or markets in my portfolio"],"best_for":["Agencies managing 10+ concurrent campaigns seeking portfolio-level optimization","Multi-product companies running parallel campaigns across different verticals","Teams with mature analytics infrastructure looking to extract strategic insights"],"limitations":["Pattern recognition quality degrades with heterogeneous data (campaigns across very different industries or platforms may produce noise)","No visibility into how many campaigns are required for statistically significant clustering","Assumes performance data is clean and consistently structured across all campaigns"],"requires":["Minimum 10-20 campaigns with complete performance data","Consistent data schema across all campaigns","Access to QuantPlus multi-campaign analysis features"],"input_types":["performance metrics across multiple campaigns (JSON/CSV)","campaign metadata (product, audience, platform, date range)","creative asset attributes or tags"],"output_types":["cluster assignments with performance rankings","pattern summaries (text descriptions of successful campaign archetypes)","cross-campaign insights (JSON with attribute correlations)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quantplus__cap_2","uri":"capability://data.processing.analysis.audience.segment.performance.attribution","name":"audience-segment-performance-attribution","description":"Disaggregates campaign performance metrics by audience segment (demographic, behavioral, geographic) and attributes performance variance to specific segment characteristics. Uses statistical analysis or gradient boosting to isolate which audience attributes drive performance differences, producing segment-level insights that inform both creative direction and media buying strategy.","intents":["I want to know which audience segments are driving my best ROI so I can allocate budget accordingly","I need to understand if my creative resonates differently across age groups, locations, or interests","I'm trying to identify underperforming segments that might need different creative messaging"],"best_for":["Performance marketers optimizing media spend allocation across segments","Creative teams tailoring messaging for specific audience personas","Agencies reporting segment-level performance to clients"],"limitations":["Requires granular audience data (demographics, interests, behaviors) which may not be available from all ad platforms","Attribution is correlational, not causal — cannot distinguish between segment quality and creative fit without controlled experiments","Small segments may produce statistically unreliable results due to low sample sizes"],"requires":["Campaign data with audience segment breakdowns","Minimum 1000+ impressions per segment for statistical reliability","Access to QuantPlus audience analysis features"],"input_types":["performance metrics by audience segment (JSON/CSV)","segment definitions (demographics, interests, behaviors)","creative asset assignments per segment"],"output_types":["segment performance rankings (JSON with metrics and rankings)","segment-specific creative recommendations (text)","budget allocation suggestions (JSON with recommended spend distribution)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quantplus__cap_3","uri":"capability://planning.reasoning.creative.hypothesis.generation.and.prioritization","name":"creative-hypothesis-generation-and-prioritization","description":"Generates ranked lists of specific creative hypotheses (e.g., 'test benefit-focused headlines with audience X', 'try video format instead of static for segment Y') based on performance data analysis and pattern recognition. Uses reinforcement learning or decision trees to prioritize hypotheses by estimated impact and feasibility, enabling teams to focus testing efforts on highest-potential variations.","intents":["I want a prioritized list of creative tests to run next based on my data, not my intuition","I need to brief my team on which creative changes are most likely to improve performance","I'm trying to decide between 10 potential creative directions — which should I test first?"],"best_for":["Agile marketing teams running rapid A/B testing cycles","Agencies needing to justify creative recommendations to clients with data","Teams with limited testing budget who need to maximize learning per test"],"limitations":["Hypotheses are generated from historical data patterns; may miss novel creative directions not represented in past campaigns","Prioritization assumes past performance is predictive of future performance, which may not hold in dynamic markets","No built-in mechanism to account for creative fatigue or audience saturation effects"],"requires":["Sufficient historical campaign data (likely 20+ campaigns minimum)","Clear performance metrics (CTR, conversion rate, ROAS, etc.)","Access to QuantPlus hypothesis generation features"],"input_types":["historical campaign performance data (JSON/CSV)","creative asset metadata (format, copy themes, visual style, CTA type)","audience and platform information"],"output_types":["ranked hypothesis list (JSON with hypothesis description, estimated impact, confidence score)","hypothesis briefs (text descriptions with rationale and success criteria)","test design recommendations (sample size, duration, success metrics)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quantplus__cap_4","uri":"capability://planning.reasoning.campaign.performance.forecasting","name":"campaign-performance-forecasting","description":"Predicts future campaign performance (CTR, conversion rate, ROAS) based on historical data, creative attributes, audience characteristics, and seasonal/temporal patterns. Uses time-series forecasting or regression models trained on historical campaign data to estimate expected performance for new campaigns or variations, enabling proactive optimization before launch.","intents":["I want to estimate what performance I can expect from a new campaign before I launch it","I need to predict how seasonal trends will affect my Q4 campaign performance","I'm trying to forecast ROI for a new audience segment based on similar past campaigns"],"best_for":["Performance marketers planning budgets and setting performance targets","Agencies pitching performance guarantees or benchmarks to clients","Teams optimizing campaign timing and seasonal strategy"],"limitations":["Forecasts are only as good as historical data quality and representativeness; gaps or biases in historical data propagate to predictions","Cannot account for external market shocks, competitive changes, or platform algorithm updates","Seasonal forecasting requires 1-2 years of historical data to capture full seasonal cycles","Prediction confidence intervals are likely not transparently reported, making it unclear how much to trust forecasts"],"requires":["12+ months of historical campaign data for seasonal pattern detection","Consistent performance metrics across all historical campaigns","Access to QuantPlus forecasting features"],"input_types":["historical campaign performance time series (JSON/CSV)","creative and audience attributes for new campaign","seasonal/temporal context (date, day of week, holidays)"],"output_types":["performance forecast (JSON with point estimate and confidence interval)","forecast explanation (text describing key drivers and assumptions)","sensitivity analysis (JSON showing how forecast changes with different inputs)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quantplus__cap_5","uri":"capability://text.generation.language.natural.language.insight.generation","name":"natural-language-insight-generation","description":"Converts raw performance data and statistical analysis results into natural language insights and recommendations that non-technical stakeholders can understand. Uses large language models or templated generation to produce narrative summaries of data patterns, creative recommendations, and strategic implications, bridging the gap between data science outputs and business communication.","intents":["I want to explain campaign performance insights to my client in clear, non-technical language","I need to write a weekly performance report that translates metrics into actionable recommendations","I'm trying to communicate data-driven creative direction to my design team without overwhelming them with statistics"],"best_for":["Agency account managers communicating with non-technical clients","Marketing leaders translating analytics insights for executive stakeholders","Teams automating performance report generation"],"limitations":["Generated insights are only as accurate as underlying data analysis; garbage in, garbage out","Natural language generation may oversimplify complex statistical relationships or miss important caveats","No transparency on how insights are selected or prioritized for inclusion in reports","Risk of generating misleading narratives if LLM hallucinates or misinterprets data patterns"],"requires":["Structured data analysis results (JSON with metrics, patterns, recommendations)","Access to QuantPlus insight generation features","Optionally: custom templates or brand voice guidelines"],"input_types":["structured analysis results (JSON with metrics, patterns, statistical significance)","campaign metadata (product, audience, platform, date range)","optional: custom tone/style preferences"],"output_types":["narrative insights (text summaries of key findings)","recommendation briefs (text descriptions of suggested actions with rationale)","executive summaries (formatted text suitable for reports or presentations)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quantplus__cap_6","uri":"capability://tool.use.integration.ad.platform.data.integration.and.normalization","name":"ad-platform-data-integration-and-normalization","description":"Connects to ad platforms (Google Ads, Facebook Ads, LinkedIn, etc.) via native APIs or data connectors to automatically ingest campaign performance data, creative metadata, and audience information. Normalizes heterogeneous data schemas across platforms into a unified internal format, enabling cross-platform analysis and comparison without manual data wrangling.","intents":["I want to analyze performance across Google Ads, Facebook, and LinkedIn campaigns in one place","I need to automatically sync my latest campaign data without manual CSV exports","I'm trying to compare performance metrics across platforms that use different naming conventions"],"best_for":["Agencies managing multi-platform campaigns for clients","Performance marketers running campaigns across 3+ ad platforms","Teams seeking to eliminate manual data export and consolidation workflows"],"limitations":["Integration coverage is unknown; likely supports major platforms (Google, Meta) but may not support niche platforms or custom ad systems","Data freshness depends on platform API rate limits and sync frequency; real-time data unlikely","Normalization may lose platform-specific metrics or nuances in how metrics are calculated","Requires API credentials and OAuth permissions for each platform, adding setup complexity"],"requires":["Active accounts on supported ad platforms (Google Ads, Facebook Ads, etc.)","API credentials or OAuth access for each platform","QuantPlus account with integration features enabled"],"input_types":["ad platform API connections (OAuth tokens)","optional: custom field mappings for non-standard metrics"],"output_types":["unified campaign performance data (JSON/CSV with normalized schema)","integration status and sync logs (JSON with last sync time, record counts)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quantplus__cap_7","uri":"capability://data.processing.analysis.interactive.performance.dashboard.and.exploration","name":"interactive-performance-dashboard-and-exploration","description":"Provides an interactive web-based dashboard for exploring campaign performance data, filtering by dimensions (audience, platform, date range, creative attributes), and drilling down into specific campaigns or segments. Likely uses client-side visualization libraries (D3, Plotly) or BI tool integrations to enable fast, responsive exploration without requiring SQL knowledge or data science expertise.","intents":["I want to quickly explore which campaigns performed best without writing SQL queries","I need to filter performance data by audience segment and date range to find trends","I'm trying to drill down from portfolio-level insights to individual campaign details"],"best_for":["Non-technical marketers exploring performance data independently","Teams needing ad-hoc data exploration without data analyst involvement","Agencies presenting performance data to clients in interactive format"],"limitations":["Dashboard performance may degrade with large datasets (100k+ campaigns or rows); no information on scaling limits","Customization options for dashboard layout and metrics are likely limited in free tier","No indication of whether dashboards can be embedded or shared externally","Real-time data updates depend on underlying data sync frequency"],"requires":["QuantPlus account with dashboard access","Campaign data synced to QuantPlus platform","Modern web browser (Chrome, Firefox, Safari, Edge)"],"input_types":["campaign performance data (from platform integrations or manual uploads)","optional: custom filters or dimension definitions"],"output_types":["interactive visualizations (charts, tables, heatmaps)","filtered data exports (CSV/JSON for selected campaigns or segments)","drill-down reports (detailed metrics for selected campaigns)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Historical campaign performance data (CTR, conversion rate, spend, impressions minimum)","Access to QuantPlus platform (free tier available)","Campaign data in supported format (likely CSV, JSON, or native platform API)","Minimum 10-20 campaigns with complete performance data","Consistent data schema across all campaigns","Access to QuantPlus multi-campaign analysis features","Campaign data with audience segment breakdowns","Minimum 1000+ impressions per segment for statistical reliability","Access to QuantPlus audience analysis features","Sufficient historical campaign data (likely 20+ campaigns minimum)"],"failure_modes":["Requires sufficient historical campaign data (likely minimum 20-50 campaigns) for meaningful pattern detection; sparse data yields generic recommendations","No transparency on which performance metrics are weighted in the analysis or how creative attributes are classified","Likely limited to specific ad platforms or data formats; integration with custom analytics systems unknown","Pattern recognition quality degrades with heterogeneous data (campaigns across very different industries or platforms may produce noise)","No visibility into how many campaigns are required for statistically significant clustering","Assumes performance data is clean and consistently structured across all campaigns","Requires granular audience data (demographics, interests, behaviors) which may not be available from all ad platforms","Attribution is correlational, not causal — cannot distinguish between segment quality and creative fit without controlled experiments","Small segments may produce statistically unreliable results due to low sample sizes","Hypotheses are generated from historical data patterns; may miss novel creative directions not represented in past campaigns","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.25,"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":"2026-05-24T12:16:32.438Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=quantplus","compare_url":"https://unfragile.ai/compare?artifact=quantplus"}},"signature":"a3fEs3UmcvxPiaVO45ocaAD10jvYweAkaZwzgIIGxjv9dwQKP5l9gx2qUbF31JsHHS3zW8HaVZQ1tUDhVvtiAQ==","signedAt":"2026-06-20T17:53:45.755Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/quantplus","artifact":"https://unfragile.ai/quantplus","verify":"https://unfragile.ai/api/v1/verify?slug=quantplus","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"}}