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Catalog & data

Q: What’s the difference between a Catalogue and a dataset? A: A Catalogue is persistent — it has a defined schema (Attributes), versioned records, data sources, and a full history with provenance. Operations write back to it on a schedule or in a pipeline. A dataset (from a Research Agent) is a one-off output; it exists until you promote it into a Catalogue. Q: Can I have multiple catalogues in one workspace? A: Yes. Catalogues can also reference each other — for example, Product.supplier → Supplier. Cross-catalogue references enable filtering, enrichment, and denormalization across related objects. Q: How do I handle large catalogs (millions of records)? A: Use database connectors (BigQuery, Postgres, Supabase) or S3 as Data Sources rather than file uploads. Operations run in parallel batches server-side. Contact us for guidance on sharding and scheduling for very large volumes. Q: What file formats are supported for import? A: CSV and XLSX for file upload. Structured JSON and CSV via HTTPS pull and database connectors. PDFs for Knowledge Bases (Word, PowerPoint, and plain text coming soon).

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Operations

Q: How is the confidence score computed? A: Confidence combines four factors: source reliability (40%), content consistency (30%), model certainty (20%), and retrieval quality (10%). See Confidence Scoring for details. Q: What happens if an operation fails partway through? A: Failed rows are retried automatically. Rows that continue to fail after retry consume zero credits. The run shows a partial success count and per-row error details. Q: Can I roll back a bulk enrichment that went wrong? A: Yes. Every write is reversible from the record’s per-field history. Roll-back is per-field and is itself a logged event with your identity as the source. Q: What does “chained pipeline” mean? A: You can configure operations to trigger each other in sequence — for example, Data Source Mapping → Validate Data → Normalize Data → Bulk Enrichment → Push & Sync. Each stage gates the next; a failed validation stops the pipeline before bad data reaches the catalog.

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Plans & billing

Q: Do unused subscription credits roll over? A: No. Monthly subscription credits reset each billing cycle. Top-up pack credits never expire. Q: Can I cancel my subscription at any time? A: Yes. Cancel from Settings → Billing. You retain access until the end of the current billing period. Q: Can I supply my own OpenAI API key? A: This is planned for a future release. Currently all AI processing runs through Claro’s managed infrastructure. Q: Is self-hosting available? A: Yes, on Dedicated plans. We provide a Kubernetes Helm chart or can set up a managed VPC in your environment. See Security & Compliance for details.

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Research Agents

Q: Can I turn a Research Agent dataset into a persistent catalog? A: Yes. From any Generated Dataset, choose Promote to Catalogue, pick the target catalogue, and map columns to attributes. The records become persistent and subject to all the same operations, history, and review workflows as native catalogues. Q: Are Research Agents the same as the old Claro product? A: Research Agents are the evolved form of the original dataset-generation features. They’ve been expanded with new agent types and now sit alongside the persistent Catalogue as one of two complementary surfaces on the platform.

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Security & compliance

Q: Where is my data stored? A: EU and US data residency options are available. Region is selected at workspace creation. Q: Is Claro SOC 2 certified? A: A SOC 2 Type II audit is in progress; expected completion Q4 2025. DPA available on request. Q: How do I get support? A: Free and Starter plans include community support via our Slack channel. Dedicated plans include a dedicated success team. Email hello@getclaro.ai for anything urgent.