Claro

What is Claro (Research Agents)?

Claro’s Research Agents are an AI research tool designed to automate manual research work directly inside a table. The agent can generate and enrich datasets, research companies, monitor changes such as pricing, and extract structured data from documents, with outputs structured for downstream use.

The page positions Research Agents as “standalone” (usable independently) while also being “stronger inside Claro,” where it can align with an existing “master table” workflow. The core purpose is to keep research outputs structured, traceable, and delivered in the same dataset teams already use.

Key Features

• Native table workflow (start from your dataset): Start with an imported CSV, supplier catalog, generated dataset, or a blank table so research outputs remain within the same tabular structure.
• Row-based execution with scaling controls: Add a column, define the task, and run across small samples (e.g., 10 rows) or very large sets (e.g., 100,000 rows) without switching tools.
• Generate and enrich lists: Scan sources (including websites, as described) to enrich lists with structured datapoints.
• Document processing for structured extraction: Upload PDFs or contracts to extract structured data into table-ready fields.
• Monitoring for pricing/availability changes: Track pricing, availability, and changes across sources in real time (as described) to keep datasets current.
• Classification and tagging: Automatically categorize and tag using custom logic defined inside the table.
• Claro-connected mode for entity-aware outputs: When connected to Claro, the agent is described as entity-aware and aligned to canonical IDs, with synchronization to systems such as ERP, PIM, ecommerce, and analytics; it’s also described as governed with audit trails and review queues.

How to Use Claro (Research Agents)

1. Create or import your table: Begin with an imported CSV, supplier catalog, generated dataset, or a blank table inside Claro.
2. Choose the research task: Select an agent capability such as list enrichment, document processing/extraction, classification/tagging, or monitoring.
3. Define the criteria and run: Add a column for the output you want, describe the task/criteria in natural language (where applicable), and run the agent across your selected rows.
4. Review structured outputs: Use the table results and (when connected to Claro) benefit from traceability and governance features such as audit trails and review queues before finalizing downstream updates.

Use Cases

• List enrichment for operational research: Enrich an existing list with structured datapoints by scanning relevant websites, keeping results in the same table format.
• Company research and dataset expansion: Research companies based on criteria you provide and generate enriched, verified dataset rows rather than unstructured text outputs.
• Pricing and availability monitoring: Monitor pricing, availability, and changes across sources in real time and update your dataset as changes occur.
• Contract/PDF structured extraction: Upload PDFs or contracts and extract key structured fields into a table for easier analysis and downstream processing.
• Categorization and tagging at scale: Apply custom classification logic to automatically categorize and tag items directly within your dataset.

FAQ

Can the Research Agent be used on its own?

Yes. The page states that the Research Agent can be used independently as a structured research tool.

What input formats can I start with?

Claro’s Research Agents can start from an imported CSV, a supplier catalog, a generated dataset, or a blank table.

Where do the outputs go?

Outputs run directly inside the native table interface, producing structured results in table form (“structured in, structured out” as described on the page).

What kinds of data can be extracted from documents?

The page specifically mentions uploading PDFs or contracts to extract structured data.

Does Claro improve the agent when connected?

The page describes additional behavior when connected to Claro, including entity-aware, canonical-ID alignment, synchronization with ERP/PIM/ecommerce/analytics systems, and governance with audit trails and review queues.

Alternatives

• General-purpose AI extractors (document-to-structure tools): Useful if your main need is extracting fields from PDFs/contracts, but they may not be designed for the same table-first, dataset-native workflow.
• Web scraping and ETL pipelines: Can gather information from websites and load into data systems; however, they typically require more engineering to convert results into validated, structured table outputs.
• Data catalog/enrichment platforms: Focused on enriching and standardizing entity data; depending on tooling, they may emphasize data quality workflows rather than running research directly inside a table.
• BI workflows with manual research steps: Useful for analysis once data is ready, but they don’t directly automate the research, extraction, and monitoring steps described for Claro’s Research Agents.