> ## 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.

# Quick Start Guide

> Stand up your first catalogue and run an operation against it in under 10 minutes.

This guide walks through the catalogue-first flow: create a catalogue, connect a data source, run operations, review the changes, and sync downstream. If you only need a one-off dataset, skip ahead to [Research Agents](/research_agents).

### Step 1 · Create a catalogue (1 minute)

* From the **Dashboard**, choose **Catalog Operations → Onboard & enrich SKUs** (or click **Start managing your catalog**).
* Name the catalogue (e.g. *Products*, *Suppliers*) and pick a starting object type. Each catalogue is opened with five tabs: **Attributes**, **Records**, **Data Source**, **Operation**, **Config**.

### Step 2 · Define your attributes (1–2 minutes)

* On the **Attributes** tab, add the typed fields your records need: text, number, image, date, enum, reference.
* Mark fields as required, set enum values, and configure references between catalogues (e.g. `Product.supplier` → `Supplier`).
* You can edit the schema later — Claro tracks attribute history alongside record history.

### Step 3 · Connect a data source (2 minutes)

* On the **Data Source** tab, pick an upstream input:
  * File upload (CSV, XLSX)
  * Scheduled scrape or HTTPS pull
  * Supplier Portal submission
  * Database connector (BigQuery, Postgres, Supabase)
* Map the source columns to your attributes. The mapping is saved per source and reused on subsequent syncs.

### Step 4 · Run your first operation (2–3 minutes)

Open the **Operation** tab. The operation grid shows everything you can run against this catalogue. A typical first run:

1. **Validate Data** — surfaces schema issues, missing required fields, and rule violations.
2. **Normalize Data** — standardizes units, currencies, dates, and casing across the records.
3. **Bulk Enrichment** — fills missing attributes from web search, model generation, supplier docs, or another catalogue.

Each operation produces a run with a reversible diff and a confidence score per change.

### Step 5 · Review and apply (1–2 minutes)

* Above-threshold writes are auto-applied.
* Below-threshold writes queue in **Notifications** for human review.
* Review items can be approved, rejected, or edited inline; bulk actions are available per cluster.

### Step 6 · Sync downstream (1 minute)

* Under **Distribute**, configure **Sync & Export** with the connectors you need: Shopify, Amazon, BigQuery, Google Sheets, Webhooks, S3.
* Or expose the **Unified Catalog** as a query-ready view for downstream consumers.

### Tips

* Start small — load 50–100 records, run the operations end-to-end, and tune the thresholds before scaling up.
* Chain operations into a pipeline (Onboard → Validate → Normalize → Bulk Enrich → Push & Sync) so each new batch of records flows through the same gates.
* Every value carries provenance — the operation, run, source, model, and reviewer that produced it. Roll-back is per-field.

Ready to go deeper? See [Catalogue](/catalogue), [Operations](/operations), and the module guides for [Onboard](/modules/onboard), [Analyse](/modules/analyse), [Monitor](/modules/monitor), and [Distribute](/modules/distribute).