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Documentation Index

Fetch the complete documentation index at: https://docs.getclaro.ai/llms.txt

Use this file to discover all available pages before exploring further.

AI Tasks are the building blocks behind Claro’s operations and Research Agents. They’re rarely invoked directly anymore — Operations like Bulk Enrichment and Generative Engine wrap them in catalogue-aware workflows with confidence, provenance, and review built in. This page documents each capability so you can choose the right one when configuring an operation or a Research Agent run.

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Capabilities

  • Classification
  • Generation
  • Web Enrichment
  • Geolocation Enrichment
  • File Extraction
  • Advanced OCR

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Model selection

Most tasks support multiple models. Claro’s proprietary model is the default — it generates confidence scores and citations alongside outputs, which makes it the right choice when a task feeds back into a catalogue. Other models are available for specialized use cases with variable pricing.

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Credit consumption

Credits are consumed per record. Cost depends on the model and the task type. Failed tasks consume zero credits.

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Classification

Categorize and tag records with consistent labels. How it works — compares each record against your defined categories or asks the model to suggest classifications based on patterns in your data. Used by — Bulk Enrichment with a target enum or reference attribute, Generate Taxonomy proposal review, Validate Data rule routing. Tips
  • Works best with ≤ 50 categories per task; for larger label sets use Generate Taxonomy first to build hierarchy.
  • Provide 2–3 examples per category in the prompt.
  • Adjust the confidence threshold per attribute, not globally.

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Generation

Create or rewrite text content tailored to your specifications. How it works — language models produce new content based on the prompt and other attributes on the record. Used by — Generative Engine (descriptions, marketing copy, alt-text, translations), SEO Report (suggestions). Tips
  • Specify exact length and format constraints.
  • Reference other attributes with @attribute_name to ground generation.
  • Attach Knowledge Bases for brand voice and category consistency.

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Web Enrichment

Research live information from across the web to enhance records. How it works — searches the web for relevant information, extracts key details, and provides citations. Used by — Bulk Enrichment with web search as the source. Tips
  • Cannot access paywalled or private content.
  • Quality scales with how well-known the entity is.
  • Always keep citations on; review at least the bottom 10% by confidence before raising auto-apply.

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Geolocation Enrichment

Transform addresses into rich geographic data and nearby points of interest. How it works — converts addresses to coordinates and optionally finds surrounding businesses and amenities. Used by — Bulk Enrichment for address-bearing catalogues, the Find your perfect list Research Agent. Tips
  • Less accurate for rural or remote locations.
  • P.O. boxes cannot be geocoded.
  • Use a tight radius (1–2 km) in dense urban areas.

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File Extraction

Extract structured data from PDFs, invoices, forms, and documents — without templates. How it works — AI-powered document parser identifies and extracts specific fields against a target schema. Used by — Turn documents into structured data Research Agent, Bulk Enrichment when the source is a Knowledge Base of supplier datasheets. Tips
  • Specify the exact fields you need.
  • Group related documents into one Knowledge Base for re-use across many extractions.
  • Use Advanced OCR for low-quality scans, handwriting, or complex layouts.

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Advanced OCR

Extract text from low-quality, handwritten, or complex visual documents. How it works — vision-AI pipeline optimized for documents that standard OCR cannot handle. Used by — File Extraction as a fallback, Turn documents into structured data with the advanced OCR option enabled. Tips
  • Try standard File Extraction first — it’s faster and cheaper.
  • Improve scan quality before extraction when you can.
  • Break very long documents into sections.

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Setting up an AI task inside an operation

When you configure Bulk Enrichment or Generative Engine, the same building blocks appear:
  • Prompt — reference attributes with @attribute_name. Be specific about format and length.
  • Knowledge Bases — attach domain documents for grounding and citation.
  • Model — choose Claro default for confidence + citations, or another model for specialized cases.
  • Confidence thresholds — auto-apply, review, reject. Defaults are conservative.
  • Output format — text, structured (per the target attribute’s type), or multi-value.

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Where AI tasks fit in the platform

SurfaceHow AI tasks are used
Catalogue OperationsWrapped in operations (Bulk Enrichment, Generative Engine, Validate, Normalize, Find Duplicates, etc.) with confidence, provenance, and review.
Research AgentsWrapped in agent flows for one-off datasets — list-building, document parsing, scraping, spreadsheet enrichment.
Knowledge BasesAttached to operations and agents to ground outputs in domain documents.
For most users the right entry points are Operations and Research Agents. This page is the underlying reference.