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| title: everyrow annotate – AI Data Annotation & Web Research Agents | |
| emoji: 🏷️ | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: "5.49.1" | |
| app_file: app.py | |
| pinned: false | |
| short_description: AI-powered data annotation powered by webresearch agents | |
| # 🏷️ everyrow annotate – AI Agents for Data Annotation & Web Research | |
| **everyrow annotate** is an AI-powered data annotation and enrichment tool that uses web research agents to label, verify, and enrich datasets at scale. | |
| It’s built for teams who need **scalable data annotation**, **evidence-backed labeling**, and **agentic research workflows** across large datasets. | |
| Agents are tuned on [Deep Research Bench](https://everyrow.io/), our benchmark for questions that need extensive searching and cross-referencing. | |
| Get your API key from [everyrow.io/api-key](https://everyrow.io/api-key) ($20 free credit). | |
| See [everyrow.io](https://everyrow.io/) for docs and [everyrow-sdk](https://github.com/futuresearch/everyrow-sdk) for the GitHub repository. | |
| ## 🧩 What Can You Annotate with everyrow? | |
| everyrow Annotate is designed for **row-by-row labeling and enrichment**, including: | |
| - 🏷️ **Data annotation** – Classify companies, products, or people using web evidence | |
| - 🌍 **Web-backed labeling** – Tag rows based on information found across multiple sources | |
| - 📊 **Dataset enrichment** – Add funding, industry, location, or other attributes | |
| - 🧠 **AI-assisted verification** – Check if claims about an entity are true | |
| - 🔎 **Research-based features** – Turn unstructured web info into structured columns | |
| If your workflow involves *“add labels or attributes to every row in my dataset”*, everyrow Annotate is built for you. | |
| ## ⚙️ How It Works | |
| 1. **Upload a CSV** with the rows you want to annotate | |
| 2. **Write an annotation instruction** (e.g., “Label each company’s industry and whether it is B2B or B2C”) | |
| 3. everyrow’s **AI research agents** search the web, compare sources, and extract structured labels | |
| 4. Download your **annotated and enriched dataset** | |
| Unlike traditional labeling tools that rely only on human input or static models, everyrow uses **live web research** to support complex, evidence-based annotations. | |
| ## 🤖 Agentic Research at Scale | |
| everyrow Annotate uses autonomous AI research agents that can: | |
| - Perform multi-step web searches | |
| - Cross-reference multiple sources | |
| - Resolve conflicting information | |
| - Produce structured, explainable outputs | |
| This enables **high-quality data annotation** for tasks that go beyond simple classification — especially when answers require investigation, not just pattern matching. | |
| ## 🔌 Powered by the everyrow SDK | |
| This demo runs on the **[everyrow SDK](https://github.com/futuresearch/everyrow-sdk)**, which provides APIs for scalable AI data annotation, web research automation, and dataset enrichment. | |
| Get an API key at **[everyrow.io/api-key](https://everyrow.io/api-key)** (includes $20 free credit). | |
| 📚 Docs: **[everyrow.io](https://everyrow.io/)** | |