{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_predict-ai","slug":"predict-ai","name":"Predict AI","type":"product","url":"https://www.neuronsinc.com","page_url":"https://unfragile.ai/predict-ai","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_predict-ai__cap_0","uri":"capability://image.visual.audience.response.prediction.for.visual.creative.assets","name":"audience-response prediction for visual creative assets","description":"Analyzes uploaded images and visual designs using trained machine learning models to forecast quantitative audience engagement metrics (likes, shares, comments, click-through rates) before publication. The system ingests creative assets, processes them through computer vision and predictive modeling pipelines, and outputs confidence-scored predictions on audience response dimensions. This enables marketers to validate design decisions against predicted performance without live A/B testing.","intents":["I need to predict how my social media post design will perform before I publish it","I want to compare engagement predictions across multiple design variations to pick the strongest one","I need data-driven confidence scores to justify creative decisions to stakeholders before budget allocation","I want to reduce failed campaigns by validating designs against audience response models before launch"],"best_for":["social media marketing teams managing multiple campaigns across platforms","creative agencies validating client designs before presentation","in-house marketing teams with limited A/B testing budgets","brands optimizing creative spend across paid social channels"],"limitations":["Predictions are based on historical training data and may not account for novel trends or cultural shifts","Requires manual asset upload and batch processing — no real-time inline feedback during design iteration","Prediction accuracy depends on asset similarity to training distribution; highly novel or niche designs may have lower confidence","Does not provide actionable recommendations for design improvement, only prediction scores","Limited to static image analysis; does not predict video, animation, or dynamic creative performance"],"requires":["Active Predict AI account with valid API credentials or web dashboard access","Image assets in common formats (JPEG, PNG, WebP, GIF)","Minimum image resolution of 400x400px for reliable analysis","Internet connectivity for asset upload and result retrieval"],"input_types":["image (JPEG, PNG, WebP, GIF)","metadata (platform context, target audience demographics, campaign type)"],"output_types":["structured data (engagement prediction scores with confidence intervals)","numeric metrics (predicted likes, shares, comments, CTR percentages)","categorical labels (performance tier: high/medium/low engagement)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_predict-ai__cap_1","uri":"capability://image.visual.multi.platform.creative.performance.benchmarking","name":"multi-platform creative performance benchmarking","description":"Compares predicted audience response metrics across different social media platforms (Instagram, Facebook, TikTok, LinkedIn, Twitter) for the same creative asset, accounting for platform-specific engagement patterns and audience demographics. The system applies platform-specific prediction models that weight visual elements, copy length, hashtag density, and format differently based on each platform's algorithm and user behavior. This enables cross-platform creative strategy optimization without manual platform-by-platform testing.","intents":["I want to know which platform will give my design the best engagement before I post it","I need to adapt my creative for different platforms and predict performance on each","I want to understand how platform-specific algorithms will treat my design differently","I need to allocate budget across platforms based on predicted performance, not guesswork"],"best_for":["multi-channel marketing teams managing campaigns across 3+ social platforms","social media agencies optimizing client content distribution strategy","brands with limited organic reach needing to prioritize paid promotion budgets","content creators optimizing posting strategy across platforms"],"limitations":["Platform algorithms change frequently; model predictions may lag behind algorithm updates by weeks or months","Predictions assume standard audience demographics; highly niche or international audiences may see different results","Does not account for timing, hashtag trends, or real-time cultural moments that drive engagement","Requires separate asset uploads per platform — no automatic format adaptation or cropping","Cross-platform comparison assumes consistent audience behavior, which may not hold for emerging platforms"],"requires":["Predict AI account with multi-platform prediction feature enabled","Assets formatted appropriately for each target platform (aspect ratios, dimensions)","Platform context metadata (target audience, campaign objective, posting time)"],"input_types":["image (platform-specific formats: 1080x1080 for Instagram, 1200x628 for Facebook, etc.)","platform identifiers (Instagram, Facebook, TikTok, LinkedIn, Twitter)","campaign metadata (audience segment, content category, posting time)"],"output_types":["structured comparison data (engagement predictions per platform with confidence scores)","ranked platform recommendations (platform A predicted 45% higher engagement than platform B)","platform-specific insights (visual elements that resonate on each platform)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_predict-ai__cap_2","uri":"capability://image.visual.creative.asset.batch.prediction.with.confidence.scoring","name":"creative asset batch prediction with confidence scoring","description":"Processes multiple creative assets in a single batch submission, generating engagement predictions and confidence scores for each asset simultaneously. The system queues batch jobs, distributes processing across inference infrastructure, and returns results with statistical confidence intervals (e.g., 'predicted 2,500 likes ±15% confidence'). This enables rapid comparison of design variations and portfolio-wide performance forecasting without sequential API calls.","intents":["I want to upload 20 design variations and get predictions for all of them at once","I need confidence intervals on predictions to understand prediction uncertainty","I want to rank creative variations by predicted performance to pick the strongest designs","I need to validate a full campaign's creative assets before budget allocation"],"best_for":["creative teams iterating on multiple design variations rapidly","agencies managing large creative portfolios across multiple clients","marketing teams running design sprints with dozens of concepts","teams needing statistical confidence in predictions for stakeholder approval"],"limitations":["Batch processing introduces latency — results may take minutes to hours depending on queue depth and batch size","Confidence intervals are statistical estimates based on model uncertainty, not guarantees of actual performance","Large batch sizes (100+ assets) may incur additional processing fees or rate limits","No real-time feedback during batch processing — results are returned as a complete set after processing completes","Confidence scores do not account for external factors (timing, trends, paid promotion) that affect actual engagement"],"requires":["Predict AI account with batch prediction API access or web dashboard","Multiple image assets in supported formats (JPEG, PNG, WebP)","Batch size within platform limits (typically 10-100 assets per batch)","Metadata for each asset (platform, audience segment, campaign type) if platform-specific predictions needed"],"input_types":["image batch (multiple JPEG, PNG, WebP files)","asset metadata (platform, audience, campaign context for each asset)","batch configuration (prediction type, confidence level, output format)"],"output_types":["structured batch results (JSON or CSV with predictions for each asset)","confidence intervals (lower bound, point estimate, upper bound for each metric)","ranked asset list (assets sorted by predicted engagement)","comparison matrix (side-by-side performance predictions across assets)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_predict-ai__cap_3","uri":"capability://image.visual.audience.demographic.response.segmentation","name":"audience demographic response segmentation","description":"Predicts how different audience demographic segments (age, gender, location, interests, income level) will respond to creative assets, enabling segment-specific engagement forecasting. The system applies demographic-aware prediction models that account for how visual elements, color schemes, messaging, and imagery resonate differently across demographic groups. Results are returned as segment-specific engagement predictions, allowing marketers to understand which demographics will engage most with each design.","intents":["I want to know if my design will resonate with my target demographic before I launch","I need to predict how different age groups or genders will respond to my creative","I want to validate that my design appeals to my intended audience, not a different segment","I need to understand which demographic segments will drive the most engagement for each design"],"best_for":["brands with clearly defined target demographics needing to validate creative appeal","agencies managing campaigns for multiple demographic segments simultaneously","consumer brands optimizing creative for specific age groups or geographic markets","teams needing to ensure inclusive design that resonates across diverse audiences"],"limitations":["Demographic predictions rely on training data that may reflect historical biases or stereotypes","Predictions assume demographic homogeneity within segments; intersectional identities are not modeled","Requires explicit demographic targeting parameters; cannot infer demographics from image content alone","Demographic response patterns may vary significantly by geographic region, limiting global applicability","Does not predict response from emerging or underrepresented demographic segments with limited training data"],"requires":["Predict AI account with demographic segmentation feature","Target demographic parameters (age ranges, gender, location, interests)","Image assets in supported formats","Sufficient historical data for target demographic segments in training dataset"],"input_types":["image (JPEG, PNG, WebP)","demographic parameters (age range, gender, location, interests, income level)","campaign context (product category, brand positioning, message tone)"],"output_types":["segment-specific predictions (engagement metrics per demographic group)","demographic affinity scores (which segments will engage most with this design)","demographic comparison matrix (side-by-side engagement predictions across segments)","demographic mismatch alerts (design predicted to resonate with unintended demographics)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_predict-ai__cap_4","uri":"capability://image.visual.creative.asset.performance.attribution.and.explainability","name":"creative asset performance attribution and explainability","description":"Identifies which visual elements, design components, and creative attributes drive predicted engagement, providing explainability for why a design is predicted to perform well or poorly. The system uses attention mechanisms, feature importance analysis, or SHAP-style attribution to highlight which parts of the image (color, composition, text, imagery) contribute most to the engagement prediction. This enables designers to understand the 'why' behind predictions and iterate designs based on identified high-impact elements.","intents":["I want to understand why my design is predicted to perform well or poorly","I need to know which visual elements are driving engagement predictions","I want to identify which design changes would most improve predicted performance","I need to explain prediction reasoning to stakeholders who question the results"],"best_for":["design teams iterating on creative based on data-driven insights","agencies explaining prediction results to clients skeptical of AI recommendations","teams building design guidelines based on what actually drives engagement","designers wanting to understand AI model behavior and improve their craft"],"limitations":["Attribution methods (attention, SHAP) are approximations and may not perfectly explain model decisions","Explainability is limited to visual features; does not explain how text, hashtags, or timing interact with visuals","High-impact elements identified by attribution may be correlations rather than causal drivers of engagement","Attribution visualizations may be difficult to interpret for non-technical stakeholders","Explainability adds computational overhead; may increase prediction latency by 20-50%"],"requires":["Predict AI account with explainability feature enabled","Image assets in supported formats","Sufficient model complexity to generate meaningful attributions (simple models may have limited explainability)"],"input_types":["image (JPEG, PNG, WebP)","prediction request with explainability flag enabled"],"output_types":["attribution heatmap (visual overlay showing which image regions drive predictions)","feature importance scores (ranked list of design elements by impact on prediction)","element-specific insights (e.g., 'red color increases predicted engagement by 12%')","design recommendation summary (actionable changes to improve predicted performance)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_predict-ai__cap_5","uri":"capability://image.visual.a.b.test.design.variant.comparison.and.ranking","name":"a/b test design variant comparison and ranking","description":"Compares predicted engagement across multiple design variations of the same creative concept, ranks them by predicted performance, and identifies statistically significant differences between variants. The system ingests a set of design variations (e.g., 'red button vs blue button', 'headline A vs headline B'), generates predictions for each, and returns ranked results with statistical significance testing. This enables rapid design optimization without live A/B testing infrastructure.","intents":["I want to compare 5 design variations and pick the one predicted to perform best","I need to know if the difference between two designs is statistically significant","I want to optimize specific design elements (color, layout, copy) based on predicted performance","I need to validate design decisions with data before committing to a single creative"],"best_for":["design teams optimizing specific creative elements before launch","marketers validating design decisions with data-driven confidence","agencies comparing client design options before presentation","teams with limited live testing budget needing rapid design validation"],"limitations":["Predicted performance differences may not match actual live A/B test results due to model limitations","Statistical significance testing is based on model confidence, not actual audience data","Requires clear variant definition; ambiguous or subtle design differences may not be reliably distinguished","Does not account for interaction effects between design elements; tests single-element variations only","Predictions assume consistent audience and context; actual performance may vary by timing, platform, or audience"],"requires":["Predict AI account with variant comparison feature","Multiple design variations in supported image formats","Clear labeling of what differs between variants (e.g., 'color', 'layout', 'copy')","Minimum 2 variants, typically 3-10 variants for meaningful comparison"],"input_types":["image batch (multiple design variations as JPEG, PNG, WebP)","variant metadata (variant name, element changed, variant group)","comparison parameters (statistical significance threshold, ranking metric)"],"output_types":["ranked variant list (variants sorted by predicted engagement)","performance comparison table (side-by-side metrics for each variant)","statistical significance results (p-values, confidence intervals for pairwise comparisons)","winner recommendation (highest-performing variant with confidence score)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_predict-ai__cap_6","uri":"capability://tool.use.integration.creative.asset.upload.and.management.via.web.dashboard","name":"creative asset upload and management via web dashboard","description":"Provides a web-based interface for uploading, organizing, and managing creative assets for prediction analysis. The system supports drag-and-drop asset upload, asset tagging and organization into campaigns or projects, version history tracking, and bulk operations. Assets are stored in a project-based structure, enabling teams to organize predictions by campaign, client, or product line and retrieve historical predictions for comparison.","intents":["I want to upload my design files and get predictions through a simple web interface","I need to organize my creative assets by campaign or project","I want to track multiple versions of a design and compare their predicted performance","I need to share prediction results with team members or stakeholders"],"best_for":["non-technical marketers and designers preferring web UI over API integration","teams managing creative assets across multiple campaigns or clients","organizations needing audit trails and version history for creative decisions","teams without engineering resources to build custom API integrations"],"limitations":["Web dashboard may have slower performance than direct API calls for bulk operations","Asset storage is limited by account tier; large creative portfolios may require premium storage","No native integration with design tools (Figma, Adobe Creative Suite); requires manual export and upload","Batch upload limits may restrict simultaneous uploads of very large asset collections","Dashboard UI changes may require user retraining; API stability is typically higher"],"requires":["Active Predict AI account with web dashboard access","Modern web browser (Chrome, Firefox, Safari, Edge)","Internet connectivity for asset upload and dashboard access","Supported image formats (JPEG, PNG, WebP, GIF)"],"input_types":["image files (JPEG, PNG, WebP, GIF via drag-and-drop or file picker)","asset metadata (name, campaign, tags, description)","bulk operations (CSV import for batch asset metadata)"],"output_types":["organized asset library (assets grouped by campaign, project, or tag)","prediction results display (engagement metrics and confidence scores per asset)","version history (previous predictions and asset versions)","shareable reports (prediction summaries exportable as PDF or CSV)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_predict-ai__cap_7","uri":"capability://tool.use.integration.integration.with.social.media.platforms.for.direct.performance.validation","name":"integration with social media platforms for direct performance validation","description":"Connects to social media platform APIs (Instagram, Facebook, TikTok, LinkedIn) to automatically retrieve actual engagement metrics for posted creative assets and compare them against Predict AI predictions. The system maps uploaded assets to published posts, collects actual engagement data post-publication, and generates accuracy reports showing how well predictions matched real-world performance. This enables continuous model improvement and prediction accuracy validation.","intents":["I want to validate how accurate Predict AI predictions are for my actual posts","I need to track prediction accuracy over time to build confidence in the tool","I want to understand which types of designs Predict AI predicts accurately vs inaccurately","I need to feed actual engagement data back to improve future predictions"],"best_for":["teams running frequent campaigns and wanting to validate prediction accuracy","agencies managing multiple client accounts and needing accuracy benchmarks","organizations building internal confidence in AI-driven creative decisions","teams with sufficient posting volume to generate statistically meaningful accuracy data"],"limitations":["Requires explicit social media platform API connections and OAuth authentication","Actual engagement metrics are available only after post publication; validation is retrospective, not predictive","Platform API rate limits and data availability windows may delay accuracy reporting by hours or days","Engagement metrics vary by posting time, audience size, and paid promotion; predictions assume organic engagement","Requires manual mapping between uploaded assets and published posts if no automated tracking is implemented","Privacy and data retention policies vary by platform; some platforms limit historical data access"],"requires":["Predict AI account with social media integration feature","Active accounts on target social media platforms (Instagram, Facebook, TikTok, LinkedIn)","OAuth authentication and API permissions for each connected platform","Sufficient posting volume to generate meaningful accuracy data (typically 10+ posts per platform)"],"input_types":["social media platform credentials (OAuth tokens)","asset-to-post mapping (linking uploaded designs to published posts)","platform-specific post metadata (post ID, publish time, audience targeting)"],"output_types":["accuracy reports (predicted vs actual engagement metrics per post)","prediction error analysis (mean absolute error, RMSE, directional accuracy)","accuracy trends (prediction accuracy over time, by asset type, by platform)","model improvement recommendations (design types where predictions are most/least accurate)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_predict-ai__cap_8","uri":"capability://data.processing.analysis.creative.asset.performance.benchmarking.against.historical.data","name":"creative asset performance benchmarking against historical data","description":"Compares predicted engagement for new creative assets against historical performance data from previously published assets, providing context for whether a design is predicted to outperform, match, or underperform past campaigns. The system indexes historical predictions and actual engagement metrics, calculates percentile rankings (e.g., 'top 15% of past designs'), and identifies design patterns that correlate with high performance. This enables designers to understand how new designs compare to proven performers.","intents":["I want to know if my new design is predicted to outperform my past successful campaigns","I need to understand what percentile my design ranks in compared to historical performance","I want to identify design patterns from my best-performing past assets","I need to set realistic performance expectations based on historical data"],"best_for":["teams with substantial historical creative portfolio wanting to benchmark new designs","brands building design guidelines based on what has worked historically","agencies comparing client designs against industry or category benchmarks","organizations tracking creative performance trends over time"],"limitations":["Benchmarking accuracy depends on historical data quality and relevance; old data may not reflect current trends","Percentile rankings assume historical data is representative; small or biased historical datasets may skew benchmarks","Does not account for external factors (seasonality, market conditions, competitive activity) that affect engagement","Requires sufficient historical data to generate meaningful benchmarks (typically 50+ past assets minimum)","Design patterns identified from historical data may reflect past trends that are no longer relevant"],"requires":["Predict AI account with historical data access","Minimum 50 historical creative assets with predictions and actual engagement data","Consistent asset categorization (product type, campaign type, audience) for meaningful benchmarking","Sufficient time period of historical data (typically 3+ months) to capture seasonal variation"],"input_types":["new creative asset (image for prediction)","historical asset library (indexed predictions and actual engagement metrics)","benchmarking parameters (comparison category, time period, audience segment)"],"output_types":["percentile ranking (e.g., 'top 20% of past designs')","benchmark comparison (predicted performance vs historical average and range)","design pattern insights (visual elements common in top-performing historical assets)","performance trend analysis (how design performance has evolved over time)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Active Predict AI account with valid API credentials or web dashboard access","Image assets in common formats (JPEG, PNG, WebP, GIF)","Minimum image resolution of 400x400px for reliable analysis","Internet connectivity for asset upload and result retrieval","Predict AI account with multi-platform prediction feature enabled","Assets formatted appropriately for each target platform (aspect ratios, dimensions)","Platform context metadata (target audience, campaign objective, posting time)","Predict AI account with batch prediction API access or web dashboard","Multiple image assets in supported formats (JPEG, PNG, WebP)","Batch size within platform limits (typically 10-100 assets per batch)"],"failure_modes":["Predictions are based on historical training data and may not account for novel trends or cultural shifts","Requires manual asset upload and batch processing — no real-time inline feedback during design iteration","Prediction accuracy depends on asset similarity to training distribution; highly novel or niche designs may have lower confidence","Does not provide actionable recommendations for design improvement, only prediction scores","Limited to static image analysis; does not predict video, animation, or dynamic creative performance","Platform algorithms change frequently; model predictions may lag behind algorithm updates by weeks or months","Predictions assume standard audience demographics; highly niche or international audiences may see different results","Does not account for timing, hashtag trends, or real-time cultural moments that drive engagement","Requires separate asset uploads per platform — no automatic format adaptation or cropping","Cross-platform comparison assumes consistent audience behavior, which may not hold for emerging platforms","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.2,"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":"2026-04-05T13:23:42.551Z","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=predict-ai","compare_url":"https://unfragile.ai/compare?artifact=predict-ai"}},"signature":"U76duuh1/78jA4sPZ7hPEG1n8Ixq/ISUh6fJhUywWXZyh94HVuj93qA3izRkeAXpUV2JX+xU56vV10ODn9PoBA==","signedAt":"2026-06-21T00:11:15.947Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/predict-ai","artifact":"https://unfragile.ai/predict-ai","verify":"https://unfragile.ai/api/v1/verify?slug=predict-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"}}