Product Introduction
Definition: Research Agents by Claro is a validation-first AI research automation platform that utilizes a suite of 10+ task-specific autonomous agents integrated directly into a native table interface. Unlike generic Large Language Model (LLM) interfaces, this tool is categorized as an "Agentic Workflow Automation" platform designed for structured data enrichment, extraction, and validation.
Core Value Proposition: Research Agents exists to eliminate the manual labor associated with web research, data entry, and unstructured document analysis. By prioritizing "validation-first" architecture over traditional "chat wrappers," the platform provides high-fidelity datasets with built-in confidence scores, citations, and ranked sources. It serves as a bridge between raw internet/document data and structured enterprise systems like ERP, PIM, and CRM.
Main Features
Task-Specific Autonomous Agents: The platform hosts a library of over 10 specialized agents including URL scraping, PDF extraction, list enrichment, geographic insight structuring, classification, and deduplication. Each agent is optimized for a narrow technical objective, ensuring higher accuracy compared to general-purpose AI.
Native Table Interface with Columnar Execution: Research Agents functions as a "spreadsheet on steroids." Users can define a task within a column (e.g., "Extract product dimensions from this URL") and run that logic across thousands of rows simultaneously. This replaces the need for custom Python scraping scripts or manual copy-pasting.
Validation-First Citations and Confidence Scoring: Every data point generated by the agents is accompanied by a confidence score and a list of ranked sources. This technical transparency allows users to audit AI outputs instantly, ensuring that the data meets enterprise-grade accuracy standards and is traceable to its origin.
Advanced Document and PDF Processing: The platform includes an OCR and LLM-powered extraction engine capable of reading complex PDFs, contracts, and supplier catalogs. It converts unstructured text into clean, tabular data fields defined by the user’s schema.
Entity-Aware Integration and Canonical Alignment: When integrated into the broader Claro ecosystem, the Research Agents become entity-aware. They can align extracted data with existing Canonical-IDs, ensuring data consistency across synced systems such as ecommerce platforms and internal analytics databases.
Problems Solved
Pain Point: Data Hallucinations and Lack of Traceability. Traditional AI research tools often "hallucinate" facts. Research Agents solve this by providing direct citations for every cell, making the research process verifiable and "audit-ready."
Target Audience:
- Catalog Managers: Who need to onboard thousands of supplier products with accurate specifications.
- Market Researchers: Who monitor competitor pricing and availability in real-time.
- Data Operations Teams: Who require structured data from unstructured sources like PDF contracts or technical manuals.
- Supply Chain Analysts: Who need to validate and enrich supplier lists with geographic and financial data.
Use Cases:
- Supplier Catalog Onboarding: Automatically extracting product attributes, images, and descriptions from PDF catalogs or manufacturer websites.
- Competitive Intelligence: Real-time tracking of pricing changes and stock levels across multiple retail websites.
- Lead Enrichment: Automatically scanning company websites to categorize business types, extract key personnel, or identify tech stacks.
- Data Cleaning and Standardization: Using the "Dedupe" and "Classify" agents to normalize messy datasets before importing them into a PIM or ERP.
Unique Advantages
Differentiation: Most AI research tools are "chat wrappers" where data is trapped in a conversation. Research Agents is built for "Structured In, Structured Out" workflows. It provides a standalone research environment that scales to 100,000+ rows, whereas chat-based tools fail at high volumes.
Key Innovation: The "Validation-First" approach is the core innovation. By ranking sources and providing confidence scores for every individual cell, Claro moves AI from a creative assistant to a reliable data utility. Its ability to sync directly with enterprise systems like ERPs and PIMs prevents the "data silo" effect common in other research tools.
Frequently Asked Questions (FAQ)
How do Claro Research Agents ensure the accuracy of scraped data? Claro utilizes a multi-step validation process where agents not only scrape the data but also cross-reference it against multiple sources. Every result is delivered with a confidence score and direct citations, allowing users to verify the information against the original source URL or document.
Can Research Agents process unstructured PDF documents into tables? Yes. The platform features a specialized Document Processing agent designed specifically for PDFs and contracts. Users can upload a document and define the specific fields they want to extract; the agent then parses the unstructured text and populates the native table with structured data.
What makes Research Agents different from a standard AI chatbot? Unlike a chatbot, which provides a single text-based response, Research Agents run inside a native table interface. This allows for bulk processing of thousands of rows of data simultaneously, producing structured outputs that are ready for export (CSV) or direct system synchronization, rather than just conversational answers.
Do I need coding skills to use these AI agents for web scraping? No. The tool is designed for operational teams and requires no coding. Tasks are defined using natural language instructions within the table interface (e.g., "Find the CEO's name from this URL"), and the agents handle the underlying web scraping, parsing, and data structuring.
How does the credit system work for Research Agents? Claro offers 200 free credits upon signup without requiring a credit card. These credits allow users to test the various agents—such as enrichment, extraction, and classification—across their own datasets to experience the speed and accuracy of the platform before committing to a paid plan.
