{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_rosetta-ai","slug":"rosetta-ai","name":"Rosetta.ai","type":"product","url":"https://rosetta.ai","page_url":"https://unfragile.ai/rosetta-ai","categories":["app-builders"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_rosetta-ai__cap_0","uri":"capability://image.visual.visual.intent.recognition.from.product.imagery","name":"visual intent recognition from product imagery","description":"Analyzes product images and customer-uploaded photos using computer vision to extract visual attributes (color, style, material, fit) and infer purchase intent without relying on browsing history. The system builds a visual embedding space that maps customer imagery to product catalog features, enabling context-aware recommendations based on what customers are looking at rather than what they've clicked. This approach uses deep learning models trained on fashion/lifestyle datasets to recognize visual patterns that correlate with conversion.","intents":["I want to show customers products visually similar to items they're viewing or uploading","I need to understand what visual attributes drive conversions in my catalog","I want to personalize recommendations based on style preferences inferred from images, not just purchase history"],"best_for":["Fashion and lifestyle ecommerce brands with rich product imagery","Merchants selling visually-differentiated products (home goods, apparel, accessories)","Teams wanting to move beyond collaborative filtering to content-based personalization"],"limitations":["Requires high-quality, consistent product photography across catalog — poor image quality degrades recognition accuracy","Visual recognition models are category-specific; cross-category recommendations (fashion to home goods) may be less accurate","Cold-start problem for new products without sufficient image training data or customer interaction signals","Computational overhead of real-time image processing may add 100-300ms latency per request at scale"],"requires":["Product catalog with minimum 500+ SKUs with images for model training","Product images in standard formats (JPEG, PNG) with minimum 300x300px resolution","API integration capability to send product images and customer context to Rosetta servers"],"input_types":["product images (JPEG, PNG)","customer-uploaded reference images","product metadata (category, attributes, price)"],"output_types":["visual similarity scores (0-1 float)","ranked product recommendations with visual reasoning","visual attribute vectors for downstream personalization"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rosetta-ai__cap_1","uri":"capability://planning.reasoning.real.time.behavioral.personalization.with.visual.context","name":"real-time behavioral personalization with visual context","description":"Tracks customer interactions (clicks, hovers, time-on-product, scroll depth) and combines behavioral signals with visual recognition to dynamically adjust product layouts, recommendations, and content in real-time. Uses a multi-armed bandit or contextual bandit algorithm to optimize which products and layouts to show each visitor based on their visual preferences and behavioral patterns, with A/B testing built into the decision loop. The system maintains per-visitor state to enable consistent personalization across sessions.","intents":["I want to show different product recommendations to different customers based on their visual preferences and behavior","I need to optimize product layout and discovery flow dynamically for each visitor","I want to run continuous A/B tests on personalization strategies without manual experiment setup"],"best_for":["Mid-market ecommerce brands with 50K+ monthly visitors (sufficient traffic for statistical significance)","Merchants with diverse product catalogs where one-size-fits-all layouts underperform","Teams wanting to move beyond static segmentation to continuous, visitor-level optimization"],"limitations":["Requires minimum traffic volume (50K+ monthly visitors) for bandit algorithms to converge and show statistical significance","Cold-start problem for new visitors with no behavioral history — may fall back to default recommendations until sufficient signals accumulate","Personalization effectiveness depends on catalog diversity and product metadata quality; sparse catalogs limit recommendation variance","Real-time decision-making adds latency (typically 50-200ms per request) which may impact page load performance if not properly cached"],"requires":["JavaScript SDK or API integration to track behavioral events (clicks, views, time-on-page)","Product catalog with at least 100+ SKUs for meaningful personalization variance","Minimum 50K monthly visitors for statistical significance in bandit optimization","Server-side session management or cookie-based visitor identification"],"input_types":["behavioral events (click, view, add-to-cart, purchase)","visitor session context (device, referrer, geography)","product metadata (category, price, inventory, visual attributes)","conversion events (purchase, add-to-cart)"],"output_types":["personalized product ranking (ordered list with scores)","layout recommendations (which products to highlight)","A/B test variant assignments","conversion probability estimates per product"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rosetta-ai__cap_2","uri":"capability://data.processing.analysis.dynamic.pricing.and.inventory.aware.recommendations","name":"dynamic pricing and inventory-aware recommendations","description":"Adjusts product recommendations and pricing in real-time based on current inventory levels, demand signals, and customer segments. The system models inventory as a constraint in the recommendation optimization function, deprioritizing low-stock items when better alternatives exist and surfacing high-inventory products to balance stock. Pricing adjustments are driven by demand elasticity models that estimate price sensitivity per customer segment, enabling margin-aware recommendations that maximize revenue rather than just conversion count.","intents":["I want to recommend products that are actually in stock and avoid showing out-of-stock items","I need to optimize pricing dynamically based on demand and inventory without manual rule configuration","I want to clear excess inventory by intelligently promoting overstocked products to price-sensitive segments"],"best_for":["Ecommerce brands with seasonal or volatile inventory patterns","Merchants with margin pressure who need to optimize revenue per visitor, not just conversion rate","Brands with multiple warehouses or SKU variants where inventory visibility is complex"],"limitations":["Requires real-time inventory feed integration — stale inventory data leads to out-of-stock recommendations and customer frustration","Dynamic pricing may trigger price-sensitive customer complaints or perception of unfair pricing if not carefully communicated","Pricing optimization models require historical demand and price elasticity data; new products or categories may use conservative defaults","Inventory-aware recommendations may reduce conversion rate in favor of revenue optimization if not properly tuned"],"requires":["Real-time inventory management system (ERP, WMS, or custom API) with sub-minute update frequency","Historical transaction data (price, quantity, customer segment) to train demand elasticity models","Product catalog with pricing and margin data","Inventory data schema including stock levels, warehouse location, and SKU variants"],"input_types":["current inventory levels per SKU","historical sales and pricing data","customer segment and price sensitivity scores","product margin and cost data","demand forecasts (optional)"],"output_types":["adjusted product recommendations with inventory-aware ranking","dynamic price suggestions per customer segment","inventory clearance recommendations","margin-optimized product bundles"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rosetta-ai__cap_3","uri":"capability://tool.use.integration.api.first.integration.with.existing.ecommerce.stacks","name":"api-first integration with existing ecommerce stacks","description":"Provides REST and webhook-based APIs to integrate Rosetta's personalization engine into existing ecommerce platforms (Shopify, WooCommerce, custom builds) without requiring months of professional services or platform migration. The system exposes endpoints for fetching personalized recommendations, tracking events, and retrieving visual analysis results, with SDKs available for common platforms. Integration follows a non-invasive pattern where Rosetta acts as a microservice that can be called on-demand rather than requiring deep platform customization.","intents":["I want to add visual personalization to my existing ecommerce platform without ripping out my current tech stack","I need to integrate Rosetta recommendations into my product pages, search results, and email campaigns via API","I want to track customer behavior and send events to Rosetta without modifying my core platform code"],"best_for":["Mid-market brands with existing ecommerce platforms (Shopify Plus, custom builds, WooCommerce)","Technical teams wanting to add personalization without vendor lock-in or platform migration","Merchants with multiple sales channels (web, mobile app, marketplace) needing a unified personalization backend"],"limitations":["API latency (50-200ms per request) may impact page load performance if not properly cached or pre-computed","Requires developer resources to implement and maintain API integrations; not a no-code solution","Event tracking accuracy depends on proper SDK implementation; missing or misconfigured events degrade personalization quality","Cross-domain personalization (web + mobile app + email) requires additional integration work and session management"],"requires":["API key and authentication credentials from Rosetta","Developer access to ecommerce platform to implement API calls","Event tracking infrastructure (custom code or tag manager) to send behavioral signals","Product catalog data in a format compatible with Rosetta's schema (JSON or CSV import)","HTTPS endpoints for webhook callbacks if using event-driven integration"],"input_types":["HTTP requests with visitor context (session ID, device, referrer)","product IDs and metadata","behavioral events (click, view, purchase) via webhooks or SDK","customer segment or cohort identifiers"],"output_types":["JSON-formatted product recommendations with scores","visual analysis results (attributes, similarity scores)","personalization metadata (experiment variant, reasoning)","webhook confirmations for event ingestion"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rosetta-ai__cap_4","uri":"capability://image.visual.visual.attribute.extraction.and.product.tagging","name":"visual attribute extraction and product tagging","description":"Automatically extracts visual attributes (color, style, material, fit, pattern) from product images using computer vision and applies semantic tags to products without manual curation. The system learns attribute patterns from your catalog and can suggest tags for new products, reducing the manual data entry burden. Extracted attributes are stored as structured metadata that feeds into visual search, recommendations, and filtering, enabling customers to search and filter by visual characteristics.","intents":["I want to automatically tag products with visual attributes (color, style, material) without manual data entry","I need to enable visual search and filtering so customers can find products by appearance","I want to standardize product metadata across my catalog to improve recommendation quality"],"best_for":["Fashion and lifestyle brands with large catalogs (1000+ SKUs) where manual tagging is impractical","Merchants wanting to improve product discoverability through visual search and attribute-based filtering","Teams with inconsistent or incomplete product metadata who need automated enrichment"],"limitations":["Attribute extraction accuracy varies by product category and image quality; fashion items are more reliable than abstract or complex products","Requires manual review and correction of extracted attributes for high-accuracy use cases; automation is not 100% reliable","Extracted attributes are limited to visual characteristics; semantic attributes (brand, occasion, target demographic) require manual tagging or additional ML models","Retraining models for new product categories or visual styles requires additional data and may take weeks"],"requires":["Product images in standard formats (JPEG, PNG) with minimum 300x300px resolution","Minimum 100-500 products per category for model training and validation","Access to product catalog database to store and update extracted attributes","Manual review process for validating extracted attributes (optional but recommended for quality)"],"input_types":["product images (JPEG, PNG)","product category or type (to select appropriate attribute extraction model)","existing product metadata (optional, for context)"],"output_types":["structured attribute tags (JSON with attribute name, value, confidence score)","visual embeddings for similarity search","attribute suggestions for new products","attribute distribution reports (e.g., 'most common colors in category')"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rosetta-ai__cap_5","uri":"capability://data.processing.analysis.conversion.lift.measurement.and.experimentation.framework","name":"conversion lift measurement and experimentation framework","description":"Measures the impact of personalization on conversion rate, average order value, and other KPIs through built-in A/B testing and statistical analysis. The system automatically assigns visitors to control (non-personalized) and treatment (personalized) groups, tracks outcomes, and computes statistical significance using frequentist or Bayesian methods. Results are reported via dashboards showing lift estimates, confidence intervals, and segment-level performance breakdowns, enabling data-driven decisions about personalization strategy.","intents":["I want to measure whether Rosetta's personalization is actually improving my conversion rate and revenue","I need to run A/B tests comparing different personalization strategies without manual experiment setup","I want to understand which customer segments benefit most from personalization"],"best_for":["Data-driven ecommerce teams with sufficient traffic (50K+ monthly visitors) for statistical significance","Merchants evaluating ROI of personalization investments and needing quantified business impact","Teams wanting to optimize personalization strategy through continuous experimentation"],"limitations":["Requires minimum traffic volume (50K+ monthly visitors) to detect meaningful lift with statistical significance; smaller sites may need weeks to reach conclusions","Lift measurement assumes proper randomization and control group isolation; implementation errors (e.g., leakage between groups) invalidate results","External factors (seasonality, marketing campaigns, competitor actions) can confound results; requires careful interpretation and domain knowledge","Bayesian methods require prior specification which may introduce bias; frequentist methods require longer experiment duration"],"requires":["Minimum 50K monthly visitors for statistical power","Proper event tracking infrastructure to measure conversions and revenue","Ability to randomly assign visitors to control/treatment groups at session level","Historical baseline conversion rate and variance data for power analysis"],"input_types":["conversion events (purchase, add-to-cart, form submission)","revenue data per transaction","visitor assignment to control/treatment groups","customer segment or cohort identifiers"],"output_types":["conversion lift estimates (percentage or absolute)","confidence intervals and p-values","segment-level performance breakdowns","statistical significance indicators","revenue impact projections"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rosetta-ai__cap_6","uri":"capability://tool.use.integration.multi.channel.personalization.orchestration","name":"multi-channel personalization orchestration","description":"Extends personalization beyond the website to email campaigns, push notifications, and marketplace listings by providing a unified API for fetching personalized recommendations across channels. The system maintains cross-channel visitor identity (matching web sessions to email subscribers to app users) and ensures consistent personalization strategy across touchpoints. Recommendations can be customized per channel (e.g., email-optimized layouts vs. mobile app layouts) while maintaining coherent customer experience.","intents":["I want to send personalized product recommendations in email campaigns based on visual preferences","I need to show consistent personalization across web, mobile app, and email without duplicating logic","I want to coordinate personalization across channels so customers see complementary products in email and on-site"],"best_for":["Ecommerce brands with mature email marketing programs and mobile apps","Merchants selling on multiple channels (own site, marketplace, social) who need unified personalization","Teams wanting to increase email engagement through visual personalization"],"limitations":["Cross-channel identity matching is imperfect; some customers may not be recognized across all channels, limiting personalization","Email personalization requires integration with email service provider (ESP) which adds complexity and latency","Marketplace personalization (Amazon, eBay) may be limited by platform restrictions on custom recommendations","Coordinating personalization across channels requires careful testing to avoid recommendation fatigue or inconsistent messaging"],"requires":["Email service provider (ESP) integration (Klaviyo, Mailchimp, custom SMTP)","Mobile app with SDK integration for event tracking and recommendation fetching","Cross-channel identity resolution (email, phone, device ID) to match customers across touchpoints","API access to marketplace platforms if personalizing marketplace listings"],"input_types":["visitor/customer identifiers (email, user ID, device ID)","channel context (web, email, app, marketplace)","channel-specific constraints (email template width, mobile screen size)","behavioral events from all channels"],"output_types":["channel-optimized product recommendations","personalization metadata (variant, reasoning, visual attributes)","email template data (JSON for dynamic content insertion)","mobile app recommendation feeds"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rosetta-ai__cap_7","uri":"capability://data.processing.analysis.visitor.segmentation.and.cohort.analysis","name":"visitor segmentation and cohort analysis","description":"Automatically segments visitors into cohorts based on visual preferences, behavioral patterns, and purchase history without manual rule definition. The system uses clustering algorithms (k-means, hierarchical clustering) on visual embeddings and behavioral features to discover natural visitor groups, then labels segments with interpretable characteristics (e.g., 'minimalist style preference', 'price-sensitive'). Segments are continuously updated as new data arrives, enabling dynamic personalization based on evolving customer preferences.","intents":["I want to understand what types of customers I have based on their visual preferences and behavior","I need to create customer segments automatically without manually defining rules","I want to tailor personalization strategy differently for different customer segments"],"best_for":["Ecommerce brands wanting to move beyond manual segmentation to data-driven cohort discovery","Merchants with diverse customer bases where one-size-fits-all personalization underperforms","Teams wanting to understand visual preference patterns across their customer base"],"limitations":["Automatic segmentation may discover clusters that are statistically significant but not actionable for business; requires interpretation and validation","Segment stability varies; clusters may shift as new data arrives, making long-term segment tracking difficult","Requires sufficient data volume (1000+ visitors per segment) for stable clustering; smaller sites may have unreliable segments","Interpretability of segments depends on feature quality; poor behavioral tracking or visual attributes lead to opaque segments"],"requires":["Minimum 10K+ visitors with behavioral data for meaningful segmentation","Visual embeddings or behavioral features for clustering (generated by Rosetta's visual recognition)","Historical purchase and interaction data for segment validation"],"input_types":["visual embeddings per visitor (from visual recognition)","behavioral features (click patterns, time-on-page, purchase history)","demographic data (optional, for validation)"],"output_types":["segment assignments per visitor (cluster ID, confidence score)","segment profiles (characteristic visual preferences, behavior patterns)","segment size and growth trends","segment-level performance metrics (conversion rate, AOV)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Product catalog with minimum 500+ SKUs with images for model training","Product images in standard formats (JPEG, PNG) with minimum 300x300px resolution","API integration capability to send product images and customer context to Rosetta servers","JavaScript SDK or API integration to track behavioral events (clicks, views, time-on-page)","Product catalog with at least 100+ SKUs for meaningful personalization variance","Minimum 50K monthly visitors for statistical significance in bandit optimization","Server-side session management or cookie-based visitor identification","Real-time inventory management system (ERP, WMS, or custom API) with sub-minute update frequency","Historical transaction data (price, quantity, customer segment) to train demand elasticity models","Product catalog with pricing and margin data"],"failure_modes":["Requires high-quality, consistent product photography across catalog — poor image quality degrades recognition accuracy","Visual recognition models are category-specific; cross-category recommendations (fashion to home goods) may be less accurate","Cold-start problem for new products without sufficient image training data or customer interaction signals","Computational overhead of real-time image processing may add 100-300ms latency per request at scale","Requires minimum traffic volume (50K+ monthly visitors) for bandit algorithms to converge and show statistical significance","Cold-start problem for new visitors with no behavioral history — may fall back to default recommendations until sufficient signals accumulate","Personalization effectiveness depends on catalog diversity and product metadata quality; sparse catalogs limit recommendation variance","Real-time decision-making adds latency (typically 50-200ms per request) which may impact page load performance if not properly cached","Requires real-time inventory feed integration — stale inventory data leads to out-of-stock recommendations and customer frustration","Dynamic pricing may trigger price-sensitive customer complaints or perception of unfair pricing if not carefully communicated","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":"2026-05-24T12:16:33.095Z","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=rosetta-ai","compare_url":"https://unfragile.ai/compare?artifact=rosetta-ai"}},"signature":"felNPQoqdIVv3qVpxFwaNSu9OHpu1ZZfGwQzk+0WnnYkSgzpejF9jHrgdo+yxe9eLnjptpjYE9nE2gm7qvoLCw==","signedAt":"2026-06-22T05:25:30.380Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rosetta-ai","artifact":"https://unfragile.ai/rosetta-ai","verify":"https://unfragile.ai/api/v1/verify?slug=rosetta-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"}}