{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_vetted","slug":"vetted","name":"Vetted","type":"product","url":"https://vetted.ai","page_url":"https://unfragile.ai/vetted","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_vetted__cap_0","uri":"capability://search.retrieval.multi.source.review.aggregation.with.source.attribution","name":"multi-source review aggregation with source attribution","description":"Vetted crawls and indexes reviews from expert publications, Amazon/retail platforms, and Reddit discussions, then normalizes heterogeneous review formats (star ratings, text sentiment, discussion threads) into a unified data model. The system maintains source provenance metadata so users can trace which review came from which platform, enabling source-aware filtering and credibility assessment without losing the original context.","intents":["I want to see what real Reddit users think about a product without visiting Reddit directly","I need expert reviews and user reviews side-by-side to make a confident purchase decision","I want to know which review sources are most trustworthy for this product category"],"best_for":["Shoppers researching mid-to-high ticket purchases ($50+) where review authenticity matters","Consumers skeptical of Amazon reviews and influencer recommendations","Researchers studying review ecosystem manipulation and astroturfing patterns"],"limitations":["Product coverage is incomplete — only products with sufficient review density across multiple sources are indexed, leaving niche items unsupported","Source-level manipulation (fake Reddit posts, paid expert reviews) is not detected or flagged; aggregation assumes source integrity","Real-time crawl lag means very recent reviews may not appear for 24-48 hours after publication","Reddit thread scraping depends on Reddit's API terms and may be rate-limited or blocked"],"requires":["Product must exist on at least 2 of: Amazon, expert review sites (Wirecutter, RTINGS, etc.), Reddit","Internet connectivity to query Vetted's backend","No API key or authentication required for free tier"],"input_types":["product name (text search)","product URL (direct link from retailer)","product category (browse mode)"],"output_types":["aggregated review summary (text)","sentiment distribution (structured: positive/neutral/negative percentages)","source breakdown (structured: count and weighting by source type)","individual review snippets with source attribution (mixed text + metadata)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vetted__cap_1","uri":"capability://text.generation.language.ai.driven.review.sentiment.synthesis.and.summarization","name":"ai-driven review sentiment synthesis and summarization","description":"Vetted uses language models to analyze review text across sources and synthesize key themes, pain points, and consensus opinions into concise summaries. The system performs aspect-based sentiment analysis (e.g., 'battery life is great but build quality is fragile') rather than single-score aggregation, allowing users to understand trade-offs without reading dozens of reviews. Summaries are regenerated per product and updated as new reviews are indexed.","intents":["I want the key pros and cons of a product without reading 50 reviews","I need to understand specific trade-offs (e.g., price vs durability) that matter for my use case","I want to know what the most common complaints are across all review sources"],"best_for":["Time-constrained shoppers making decisions in <10 minutes","Buyers with specific use cases who need to filter reviews by relevance","Non-technical consumers who find raw review data overwhelming"],"limitations":["Summarization may miss niche concerns that appear in <5% of reviews but are critical for specific use cases","LLM-based synthesis can hallucinate or misweight themes if review corpus is small (<20 reviews) or heavily skewed","Aspect extraction is limited to common product attributes; novel or category-specific concerns may not be detected","No user feedback loop to correct or weight summaries based on individual preferences"],"requires":["Minimum ~10-15 reviews per product to generate meaningful summaries","LLM API access (likely Claude, GPT-4, or proprietary fine-tuned model)","Review text must be in English (non-English reviews may be skipped or poorly summarized)"],"input_types":["raw review text (from crawled sources)","review metadata (rating, source, date)"],"output_types":["aspect-based summary (structured: {aspect: string, sentiment: positive|negative|mixed, evidence: string}[])","key themes (unstructured text summary)","consensus score per aspect (numeric 0-100)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vetted__cap_2","uri":"capability://search.retrieval.reddit.discussion.thread.retrieval.and.ranking","name":"reddit discussion thread retrieval and ranking","description":"Vetted indexes Reddit discussions (r/AskReddit, r/BuyItForLife, product-specific subreddits) mentioning products and ranks threads by relevance, recency, and engagement (upvotes, comment count). The system extracts discussion context (not just reviews) to surface authentic user conversations about product experiences, workarounds, and alternatives. Threads are deduplicated and clustered by topic to avoid showing redundant discussions.","intents":["I want to see what Redditors actually say about this product in real conversations","I need to find discussions about specific problems or use cases (e.g., 'best laptop for video editing under $1000')","I want to discover alternative products that Redditors recommend instead of the one I'm researching"],"best_for":["Shoppers who trust peer recommendations over marketing","Niche product researchers where Reddit communities are the primary knowledge source","Buyers evaluating product alternatives and trade-offs"],"limitations":["Reddit discussions are unmoderated and may contain outdated information, misinformation, or astroturfing (paid posts disguised as organic)","Indexing depends on Reddit API availability and rate limits; coverage may be incomplete for older threads","Discussions are often tangential or off-topic; ranking algorithm may surface irrelevant threads if product name is generic","No way to distinguish between verified purchases and speculation on Reddit (unlike Amazon's 'Verified Purchase' badge)"],"requires":["Product must be mentioned in at least one Reddit thread with sufficient engagement (>5 upvotes, >3 comments)","Reddit API access with rate limits (~60 requests/minute for free tier)","Subreddit indexing must be pre-configured (not all subreddits are crawled)"],"input_types":["product name or keyword (text search)","subreddit filter (optional, e.g., r/BuyItForLife)"],"output_types":["ranked list of Reddit threads (structured: {title, subreddit, upvotes, comment_count, url, snippet}[])","discussion summary (text excerpt from top comments)","sentiment from discussion (positive/negative/mixed)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vetted__cap_3","uri":"capability://search.retrieval.expert.review.source.integration.and.weighting","name":"expert review source integration and weighting","description":"Vetted integrates with expert review publications (Wirecutter, RTINGS, TechRadar, etc.) via web scraping or API partnerships, extracting structured review data (ratings, verdict, key findings) and weighting them by publication credibility and category expertise. The system maintains a credibility model per publication and product category, so a photography expert's review of a camera is weighted higher than a general tech reviewer's opinion.","intents":["I want to see what trusted experts say about this product","I need expert opinions weighted by their category expertise, not just averaged together","I want to know if expert reviews align with or contradict user reviews"],"best_for":["Shoppers buying high-ticket items ($500+) where expert credibility matters","Buyers in specialized categories (photography, audio, gaming) where expert knowledge is valuable","Researchers comparing expert consensus vs user sentiment"],"limitations":["Expert review coverage is limited to popular products; niche items may have zero expert reviews","Expert reviews are often behind paywalls or affiliate links; Vetted may have incomplete access to full reviews","Credibility weighting is opaque to users; no transparency into why one expert's opinion is weighted higher","Expert reviews are infrequently updated; a review from 2 years ago may be outdated but still weighted equally"],"requires":["Product must be reviewed by at least one major expert publication","Web scraping permissions or API partnerships with review sites (may require licensing)","Category taxonomy to map products to expert specializations"],"input_types":["product name or URL","product category (to filter relevant expert sources)"],"output_types":["expert review summary (structured: {publication, rating, verdict, key_findings}[])","credibility-weighted expert score (numeric 0-100)","expert consensus (text summary of agreement/disagreement)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vetted__cap_4","uri":"capability://safety.moderation.cross.source.review.conflict.detection.and.flagging","name":"cross-source review conflict detection and flagging","description":"Vetted compares sentiment and key findings across sources (expert vs user vs Reddit) and flags significant disagreements (e.g., 'experts rate this 9/10 but users complain about durability'). The system uses statistical methods to distinguish between legitimate trade-offs and potential review manipulation or source bias. Conflicts are surfaced to users with confidence scores and explanations.","intents":["I want to know if expert reviews contradict user reviews, which might indicate bias or astroturfing","I need to understand if negative user reviews are outliers or represent a real problem","I want to detect if a product is being artificially promoted by experts but disliked by real users"],"best_for":["Skeptical shoppers concerned about review manipulation and astroturfing","Researchers studying review ecosystem integrity","Buyers making high-stakes purchases where review authenticity is critical"],"limitations":["Conflict detection is statistical and may produce false positives if sources genuinely disagree (e.g., product is great for professionals but poor for beginners)","No access to review metadata (reviewer credentials, purchase verification) so cannot definitively prove manipulation","Requires sufficient review volume across sources to detect patterns; products with <10 reviews per source may show spurious conflicts","Cannot detect sophisticated manipulation (e.g., coordinated fake reviews that mimic authentic sentiment)"],"requires":["Minimum 10+ reviews per source to establish baseline sentiment distribution","Statistical models for anomaly detection (e.g., z-score analysis, isolation forests)","Conflict thresholds must be calibrated per product category"],"input_types":["aggregated review data across sources (sentiment, ratings, themes)"],"output_types":["conflict flags (structured: {source_a, source_b, conflict_type, confidence_score, explanation}[])","source reliability score (numeric 0-100 per source per product)","manipulation risk assessment (low/medium/high)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vetted__cap_5","uri":"capability://search.retrieval.product.search.with.natural.language.intent.understanding","name":"product search with natural language intent understanding","description":"Vetted accepts natural language product queries (e.g., 'best laptop for video editing under $1000') and uses semantic understanding to map user intent to product categories, price ranges, and use-case filters. The system disambiguates product names, handles typos and synonyms, and returns relevant products with aggregated reviews. Search results are ranked by relevance to the stated intent, not just keyword matching.","intents":["I want to search for products using natural language descriptions of my needs, not just product names","I want search results filtered by my constraints (budget, use case, brand preferences) without manual filtering","I want to discover alternative products that match my intent, not just exact name matches"],"best_for":["Casual shoppers who don't know exact product names or models","Buyers with specific use cases who need intent-aware filtering","Users discovering alternatives and comparing product categories"],"limitations":["Intent understanding may fail for ambiguous queries (e.g., 'good phone' could mean smartphone, landline, or phone case)","Search is limited to products in Vetted's index; products not yet reviewed across sources won't appear","Price range and budget constraints are extracted from text but may be inaccurate if phrased ambiguously","No personalization based on user history or preferences; all users see the same results for the same query"],"requires":["NLP model for intent extraction and entity recognition (likely transformer-based)","Product catalog with category taxonomy and attribute mappings","Semantic search index (embeddings-based) for relevance ranking"],"input_types":["natural language query (text)"],"output_types":["ranked product list (structured: {name, category, price_range, aggregated_rating, review_count}[])","applied filters (structured: {category, price_range, use_case, brand})","alternative product suggestions (structured list)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vetted__cap_6","uri":"capability://safety.moderation.review.source.credibility.scoring.and.transparency","name":"review source credibility scoring and transparency","description":"Vetted maintains a credibility model for each review source (Amazon, Reddit, expert publications) based on factors like review verification (e.g., Amazon's 'Verified Purchase'), publication reputation, community moderation, and historical accuracy. Each review or review source is assigned a credibility score (0-100) that is displayed to users, allowing them to weight reviews by trustworthiness. Scores are updated as new data becomes available.","intents":["I want to know which review sources are most trustworthy for this product","I want to filter reviews to show only verified purchases or high-credibility sources","I want transparency into why Vetted trusts some reviews more than others"],"best_for":["Skeptical shoppers concerned about fake reviews and astroturfing","Buyers making high-stakes purchases where review authenticity is critical","Researchers studying review ecosystem trust and manipulation"],"limitations":["Credibility scoring is opaque; users don't know which factors are weighted most heavily","No ground truth for review accuracy; credibility scores are based on proxy signals (verification badges, publication reputation) not actual review quality","Credibility models are static per source; no per-review credibility assessment based on content analysis","Cannot detect sophisticated manipulation (e.g., fake verified purchases, coordinated review campaigns)"],"requires":["Access to review metadata (verification badges, reviewer history, publication reputation data)","Historical data on review accuracy (difficult to obtain; may require third-party partnerships)","Credibility model calibration per product category"],"input_types":["review metadata (source, verification status, reviewer history, publication reputation)"],"output_types":["credibility score per review (numeric 0-100)","credibility score per source (numeric 0-100)","credibility factors breakdown (structured: {factor, weight, contribution}[])","credibility trend over time (time-series data)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vetted__cap_7","uri":"capability://text.generation.language.product.comparison.with.side.by.side.review.synthesis","name":"product comparison with side-by-side review synthesis","description":"Vetted allows users to select multiple products and generates side-by-side comparisons of aggregated reviews, key differences, and trade-offs. The system synthesizes reviews for each product and highlights where they differ (e.g., 'Product A has better battery life but Product B is more durable'). Comparisons include price, specs, and review-derived insights, allowing users to make informed trade-off decisions without reading individual reviews.","intents":["I want to compare two products side-by-side based on what reviewers say about them","I need to understand the key trade-offs between products (e.g., price vs quality)","I want to see which product is better for my specific use case based on review insights"],"best_for":["Shoppers deciding between 2-5 products in the same category","Buyers with specific use cases who need to understand product trade-offs","Researchers comparing product categories or brands"],"limitations":["Comparison is limited to products in Vetted's index; niche or new products may not be comparable","Review synthesis may miss category-specific attributes that matter for comparison (e.g., 'weight' for travel products)","No personalization based on user priorities; all users see the same comparison structure","Spec data (price, dimensions, weight) may be outdated or missing for some products"],"requires":["Minimum 2 products selected for comparison","Aggregated review data for each product (from previous capabilities)","Product spec data (price, dimensions, weight, etc.)","Comparison template per product category"],"input_types":["product list (2-5 products)","comparison criteria (optional: price, durability, ease of use, etc.)"],"output_types":["side-by-side comparison table (structured: {attribute, product_a, product_b, product_c}[])","trade-off analysis (text summary of key differences)","recommendation per use case (text: 'Product A is best for X, Product B is best for Y')","price-to-value analysis (structured: {product, price, review_score, value_ratio}[])"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vetted__cap_8","uri":"capability://planning.reasoning.personalized.product.recommendation.based.on.review.insights","name":"personalized product recommendation based on review insights","description":"Vetted analyzes user search history, saved products, and stated preferences to recommend products that match their needs based on review insights. The system uses collaborative filtering (if users with similar preferences liked product X, recommend it to similar users) and content-based filtering (if user liked products with attribute Y, recommend other products with Y). Recommendations are ranked by review quality and relevance to user intent.","intents":["I want product recommendations based on what reviewers say, not just popularity","I want to discover products similar to ones I've already researched","I want recommendations tailored to my specific use case and preferences"],"best_for":["Repeat users with search history and saved products","Shoppers with specific use cases who want tailored recommendations","Browsers discovering new products in familiar categories"],"limitations":["Recommendations require user history; new users see generic recommendations","Collaborative filtering may recommend popular products over niche alternatives","No explicit user feedback loop; recommendations are not personalized based on user ratings or feedback","Cold-start problem: new products with few reviews are unlikely to be recommended"],"requires":["User account with search history and saved products","Collaborative filtering model (e.g., matrix factorization, k-NN)","Content-based filtering model (e.g., product embeddings, attribute similarity)","Ranking model to combine collaborative and content-based signals"],"input_types":["user search history (implicit: products viewed, searched)","user saved products (explicit: products bookmarked)","user preferences (optional: stated budget, use case, brand preferences)"],"output_types":["ranked product recommendations (structured: {product, relevance_score, reason}[])","recommendation explanation (text: 'Based on your interest in X, you might like Y')","recommendation confidence (numeric 0-100)"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vetted__cap_9","uri":"capability://data.processing.analysis.review.trend.analysis.and.temporal.insights","name":"review trend analysis and temporal insights","description":"Vetted tracks review sentiment and key themes over time, identifying trends (e.g., 'durability complaints increased 40% in the last 3 months') and temporal patterns (e.g., 'new version released 2 months ago, reviews improved'). The system correlates review trends with product updates, recalls, or external events to provide context. Users can see if a product's reputation is improving or declining and understand why.","intents":["I want to know if a product's reputation is improving or declining over time","I want to understand if recent reviews are more positive or negative than older reviews","I want to see if a product update or recall affected user sentiment"],"best_for":["Shoppers researching products with long histories and multiple versions","Buyers concerned about recent quality issues or improvements","Researchers studying product lifecycle and reputation trends"],"limitations":["Trend analysis requires historical review data; products with <3 months of reviews show limited trends","Temporal correlation (e.g., 'sentiment improved after update') is correlational, not causal","External events (recalls, media coverage) are not automatically detected; trends may be unexplained","Review volume fluctuates over time; trends may be artifacts of sampling bias (e.g., more reviews after a recall)"],"requires":["Historical review data with timestamps (requires archiving reviews over months/years)","Product event data (release dates, updates, recalls) for correlation analysis","Time-series analysis models (e.g., ARIMA, Prophet) for trend detection"],"input_types":["review data with timestamps","product event data (optional: release dates, updates, recalls)"],"output_types":["sentiment trend over time (time-series: {date, sentiment_score, review_count}[])","theme trend over time (time-series: {date, theme, frequency}[])","trend summary (text: 'Sentiment improved 15% in the last 3 months')","correlation with events (structured: {event, date, sentiment_change}[])"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"low","permissions":["Product must exist on at least 2 of: Amazon, expert review sites (Wirecutter, RTINGS, etc.), Reddit","Internet connectivity to query Vetted's backend","No API key or authentication required for free tier","Minimum ~10-15 reviews per product to generate meaningful summaries","LLM API access (likely Claude, GPT-4, or proprietary fine-tuned model)","Review text must be in English (non-English reviews may be skipped or poorly summarized)","Product must be mentioned in at least one Reddit thread with sufficient engagement (>5 upvotes, >3 comments)","Reddit API access with rate limits (~60 requests/minute for free tier)","Subreddit indexing must be pre-configured (not all subreddits are crawled)","Product must be reviewed by at least one major expert publication"],"failure_modes":["Product coverage is incomplete — only products with sufficient review density across multiple sources are indexed, leaving niche items unsupported","Source-level manipulation (fake Reddit posts, paid expert reviews) is not detected or flagged; aggregation assumes source integrity","Real-time crawl lag means very recent reviews may not appear for 24-48 hours after publication","Reddit thread scraping depends on Reddit's API terms and may be rate-limited or blocked","Summarization may miss niche concerns that appear in <5% of reviews but are critical for specific use cases","LLM-based synthesis can hallucinate or misweight themes if review corpus is small (<20 reviews) or heavily skewed","Aspect extraction is limited to common product attributes; novel or category-specific concerns may not be detected","No user feedback loop to correct or weight summaries based on individual preferences","Reddit discussions are unmoderated and may contain outdated information, misinformation, or astroturfing (paid posts disguised as organic)","Indexing depends on Reddit API availability and rate limits; coverage may be incomplete for older threads","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"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":"2026-05-24T12:16:34.117Z","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=vetted","compare_url":"https://unfragile.ai/compare?artifact=vetted"}},"signature":"57oiRPonohyz1Bi867Mfug6umq2di12SYvttESa8CMLJ1hMa7bPWsEdvJaIPkpOI3c7WymK0GIXRKruoBq1XBA==","signedAt":"2026-06-20T07:45:33.784Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vetted","artifact":"https://unfragile.ai/vetted","verify":"https://unfragile.ai/api/v1/verify?slug=vetted","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"}}