AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment. Key Features Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more. Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes: Page state (URL, DOM snapshot, and metadata) User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions) System responses (AJAX calls, error/success messages, cart/price updates) Authentication and account linking steps where applicable Payment entry (card, wallet, alternative methods) Order review and confirmation Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments. Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines. Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions: “What the user did” (natural language) “What the system did in response” “What a successful action should look like” Error/edge case coverage (invalid forms, OOS, address/payment errors) Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations. Each flow tracks the user journey from cart to payment to confirmation, including: Adding/removing items Applying coupons or promo codes Selecting shipping/delivery options Account creation, login, or guest checkout Inputting payment details (card, wallet, Buy Now Pay Later) Handling validation errors or OOS scenarios Order review and final placement Confirmation page capture (including order summary details) Why This Dataset? Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures: The full intent-action-outcome loop Dynamic UI changes, modals, validation, and error handling Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts Mobile vs. desktop variations Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.) Use Cases LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering. Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users. Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants. UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions. Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations. What’s Included 10,000+ annotated checkout flows (retail, restaurant, marketplace) Step-by-step event logs with metadata, DOM, and network context Natural language explanations for each step and transition All flows are depersonalized and privacy-compliant Example scripts for ingesting, parsing, and analyzing the dataset Flexible licensing for research or commercial use Sample Categories Covered Grocery delivery (Instacart, Walmart, Kroger, Target, etc.) Restaurant takeout/delivery (Uber Eats, DoorDash, Grubhub, direct) General retail (Amazon, Shopify, Best Buy, electronics, apparel, etc.) Multi-merchant marketplaces (Shopify, BigCommerce) Local and national chains Niche and specialty commerce Mobile, desktop, and responsive web flows Technical Details File formats: JSONL (primary), with optional CSV, HAR, or custom export Typical flow length: 8–30+ events per session Network logs include headers, payloads, and key response values Each event cross-referenced to the visible DOM (CSS selectors, element hierarchy) All data is validated for structural consistency and completeness Availability & Customization Bulk data purchase, subscription access, and bespoke flow sampling are available. We can tailor the dataset to your specific target verticals, user segments, or technical requirements upon request.