Find local store leads in any area for any sales service (web design, SNS marketing, DX consulting, ad operations, etc.). Switch extraction mode (all stores / digital-weak only / has-website only) and result count (10-100) per query. Chain stores auto-excluded. Get your Bright Data API token at https://get.brightdata.com/tp1oc (existing promo codes still apply).

Auto-generate a Morning Brew-style daily digest from any subreddit using Bright Data scraping and GPT-4o. Input subreddit name, topic focus, and audience to get a structured newsletter with top stories, trending discussions, and editor commentary in 30 seconds. Get your Bright Data API token at https://get.brightdata.com/tp1oc (existing promo codes still apply).

Amazon の自社商品 URL と競合 5 商品の URL を入力すると、Bright Data Web Scraper API で 6 商品のメタデータ + Amazon 公式 AI 要約レビュー (`customer_says` フィールド) + 実レビュー本文 (各商品最大 20 件) を一括取得し、OpenAI GPT-4o-mini が 5 並列で各競合を「褒める点 / 不満 / 競合優位性 / 改善示唆 / 差別化案」の 5 観点で分析、3,000-5,000 字の Markdown 競合レポートを出力します。 通常 2-4 時間かかる Amazon 競合リサーチを 5-11 分に短縮、1 ラン約 $0.05-0.06。Amazon 公式が curated した `customer_says` を一次根拠、実レビュー本文を二次根拠として使うことで、生レビューを LLM に丸投げするより精度・コスト・速度のバランスが取れた設計になっています。amazon.co.jp / amazon.com 両対応。

Input 6 Amazon product URLs (yours + 5 competitors), output a Markdown competitive analysis report. Fetches metadata + Amazon's AI review summary (customer_says) via Bright Data, then runs GPT-4o-mini in 5 parallel evaluations: praise, complaints, positioning, improvements, differentiation. Why customer_says-first: Amazon's AI distills thousands of reviews into ~250 chars per product, native-language. Higher signal-to-noise than raw reviews. Pay-per-run, open YAML, amazon.co.jp validated. v0.2: customer_says-based. v0.3 plans full Reviews. Requires Bright Data + OpenAI keys. MIT.

Generate data-driven SEO content briefs by scraping Google SERP with Bright Data, analyzing top-ranking pages with OpenAI GPT-4o, and producing editor-ready briefs in Markdown.