Vetted

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.