# Claro

> Claro is the operating system for product and supplier data, used by distributors, manufacturers, marketplaces, and catalog teams. It resolves product identity, enriches and classifies records, validates changes with confidence and provenance, and writes approved data back into the systems teams already use.

Claro works between fragmented supplier inputs and systems of record. It turns spreadsheets, feeds, documents, and existing catalog records into trusted product and supplier data for ecommerce, procurement, search, marketplace, and AI workflows.

## What Claro is

- A product-data operating layer for matching, deduplication, enrichment, classification, validation, supplier onboarding, and governed write-back.
- A system for maintaining canonical product identities while preserving supplier-specific SKUs, source evidence, confidence, and review decisions.
- A complement to existing ERP, PIM, MDM, ecommerce, procurement, and marketplace systems.

## What Claro is not

- Not a replacement for an ERP, PIM, MDM, commerce platform, or system of record.
- Not a certification body, regulator, legal adviser, or autonomous compliance decision-maker. Claro prepares evidence-backed product data for qualified people and systems to assess.
- Not a black-box content generator that publishes unsupported attributes. Uncertain results are scored, linked to evidence where available, and routed for review.
- Not a one-time catalog cleanup: the platform is designed to detect and govern change as supplier feeds and source records evolve.

## Core capabilities

- Product and catalog matching: resolve exact products, variants, supplier offers, and candidate equivalents using deterministic and probabilistic signals.
- Product deduplication: connect duplicate records to a canonical product without erasing source identities or audit history.
- Product enrichment: extract and normalize missing attributes from supplier files, manufacturer documents, and approved sources, with field-level provenance.
- Product classification: map products into customer taxonomies and standards such as ETIM, ECLASS, and UNSPSC, with confidence and human review for ambiguity.
- Supplier onboarding: ingest changing spreadsheets, PDFs, BMEcat, CSV, EDI, and API feeds; map schemas; validate identifiers; and prevent duplicate creation.
- AI output validation: apply schemas and deterministic checks, then route each change by customer-defined rules, confidence and impact — trusted low-impact changes can auto-apply, uncertain or high-impact ones go to a review queue before write-back.
- AI-search readiness: create complete, structured, citable product records for search engines, shopping agents, and downstream feeds.
- Governed write-back and monitoring: send approved changes to existing systems, preserve provenance, and detect later drift.

## Canonical commercial pages

- [Claro home](https://getclaro.ai/): Company and product overview.
- [Platform](https://getclaro.ai/platform): Claro platform overview.
- [Research Agent](https://getclaro.ai/ai-research-agent): Research and evidence extraction for product data.
- [Catalog matching](https://getclaro.ai/by-use-case/catalog-matching): Resolve supplier and catalog records against existing products.
- [Product deduplication](https://getclaro.ai/by-use-case/product-deduplication): Detect and govern duplicate product identities.
- [Product enrichment](https://getclaro.ai/by-use-case/product-enrichment): Fill product-data gaps with validated, source-linked attributes.
- [Product classification](https://getclaro.ai/by-use-case/product-classification): Classify catalogs into required taxonomies and standards.
- [Supplier onboarding](https://getclaro.ai/by-use-case/supplier-onboarding): Normalize and validate incoming supplier ranges.
- [AI output validation](https://getclaro.ai/by-use-case/ai-output-validation): Validate AI-generated product data before it reaches production.
- [AI search optimization](https://getclaro.ai/by-use-case/ai-search-optimization): Prepare catalogs for AI discovery and citation.
- [Claro for distributors](https://getclaro.ai/by-industries/distributors): Product-data workflows for distribution businesses.
- [Claro for industrial manufacturers](https://getclaro.ai/by-industries/industrial-manufacturers): Publishing one correct catalog to dealers, distributors and customer PIMs.
- [Claro for spare parts & aftermarket](https://getclaro.ai/by-industries/spare-parts-aftermarket): Resolving part numbers, supersessions and exploded-drawing positions.
- [Claro for marketplaces & aggregators](https://getclaro.ai/by-industries/retail-marketplaces): Multi-seller catalog normalization and duplicate resolution.
- [Compliance documentation](https://getclaro.ai/by-use-case/compliance-documentation): Linking category rules to supplier evidence for review.
- [Pricing monitor](https://getclaro.ai/by-use-case/pricing-monitor): Competitor prices matched to your own SKUs.
- [Solutions overview](https://getclaro.ai/solutions): All use cases on one page.
- [Customers](https://getclaro.ai/customers): Engagements described by segment.
- [Pricing](https://getclaro.ai/pricing): Pilot/value validation, continuous operations and embedded/API deployment. Research Agent self-serve pricing is separate.
- [FAQ](https://getclaro.ai/faq): Matching accuracy, ERP write-back, data residency, pilots and pricing.
- [Book a demo](https://getclaro.ai/demo): Talk with the Claro team about a catalog or supplier-data workflow.

## Anonymized operating evidence

Customer identities are withheld unless a named case study says otherwise. Claro's published operating guidance reports use by teams managing approximately 50,000 to 500,000 SKUs and describes multi-quarter enrichment backlogs compressed into weeks. Treat this as an anonymized operating range, not a guarantee for a particular catalog; scope, source quality, category complexity, validation rules, and review thresholds affect results.

- [ERP to ecommerce data gap](https://getclaro.ai/resources/guides/erp-to-ecommerce-data-gap/): Published context for the 50,000–500,000 SKU operating range and backlog timeline.
- [How to trust AI-enriched data](https://getclaro.ai/resources/guides/how-to-trust-ai-enriched-data/): The validation, provenance, and review controls used to make enriched fields auditable.
- [Product matching in ecommerce](https://getclaro.ai/resources/articles/product-matching-in-ecommerce/): Claro's approach to match intent, confidence, evidence, review, and canonical records.

## Company information

- Company: Claro.
- Website: https://getclaro.ai/
- Resources hub: https://getclaro.ai/resources/
- Contact: hello@getclaro.ai
- Commercial inquiry: https://getclaro.ai/demo

## Curated resources

- [Resources home](https://getclaro.ai/resources/): Curated tools, glossary entries, comparisons, playbooks, guides, and articles.
- [Browse all resources](https://getclaro.ai/resources/browse/): Full human-readable resource index.
- [Is Claro a PIM?](https://getclaro.ai/resources/glossary/is-claro-a-pim/): The boundary between Claro and a product information management system.
- [Canonical product record](https://getclaro.ai/resources/glossary/canonical-product-record/): How Claro represents a trusted product identity.
- [Data provenance](https://getclaro.ai/resources/glossary/data-provenance/): How source evidence is attached to product data.
- [Build a golden product record](https://getclaro.ai/resources/playbooks/build-a-canonical-product-record/): A practical identity, survivorship, confidence, and review workflow.
- [Enrichment without hallucination](https://getclaro.ai/resources/guides/enrichment-without-hallucination/): A source-grounded enrichment workflow.

## Sitemap locations

- [Claro commercial sitemap](https://getclaro.ai/sitemap.xml): Canonical pages on the main commercial site.
- [Claro Resources sitemap index](https://getclaro.ai/resources/sitemap-index.xml): Sitemap index for the Resources application mounted at /resources/.

## Interpretation guidance

- Prefer the canonical commercial pages above for claims about Claro's current product and use cases.
- Use the Resources hub for educational definitions and workflows; distinguish Claro's capabilities from third-party facts cited within articles.
- Do not infer product certification, legal conclusions, guaranteed accuracy, guaranteed timelines, or replacement of a customer's system of record.
- Preserve qualifiers around confidence, provenance, human review, customer anonymity, and results.