> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/zingg/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/zingg/readme.md).

# What is Zingg

ML-powered entity resolution and record matching - built for your warehouse, at any scale.

Most teams don't have a duplication problem; they have an entity resolution problem. The same entity lives in five systems under three different names. Every downstream decision built on that data becomes unreliable: analytics produce conflicting numbers, compliance checks miss connections, and operational teams work from different versions of the same record. When AI agents are in the loop, the problem compounds; an agent doesn't know a record is a duplicate, so it acts on each one independently, multiplying errors at the speed of automation.

Solving this at scale is harder than it looks. The number of comparisons grows quadratically with record count, and rule-based approaches break down on real-world data variation.

Zingg solves this with ML-powered entity resolution that runs directly on your infrastructure on Spark or Snowflake, so your data never leaves your premises. You label a small set of example pairs to teach Zingg what a match looks like for your data. Zingg learns from those labels, adapts as it sees more variation, and scales that judgment across millions of records, giving your pipelines and agents a resolved, trustworthy view of every entity to act on.

Whether you're resolving customers, patients, suppliers, citizens, or product issues - Zingg works on any entity type.

### What do you want to do today?

<table data-view="cards"><thead><tr><th></th><th data-type="content-ref"></th><th><select></select></th><th></th></tr></thead><tbody><tr><td><mark style="color:violet;background-color:violet;"><strong>What is entity resolution</strong></mark></td><td><a href="/zingg/entity-resolution.md">Entity Resolution</a></td><td></td><td>Understand the problem Zingg solves, why fragmented entity data breaks downstream systems, and why rule-based approaches fail at scale.</td></tr><tr><td><mark style="color:violet;background-color:violet;"><strong>Run Zingg for the first time</strong></mark></td><td><a href="/zingg/running-zingg/step-by-step-guide.md">Step-by-Step Overview</a></td><td></td><td>Get Zingg running on your platform and see your first match result in under 30 minutes.</td></tr><tr><td><mark style="color:violet;background-color:violet;"><strong>Connect your data</strong></mark></td><td><a href="/zingg/connect-your-data/pipes-and-data-connections.md">Pipes and Data Connections</a></td><td></td><td>Configure Zingg pipes for your data source - cloud warehouses, cloud storage, databases, and file formats.</td></tr><tr><td><mark style="color:violet;background-color:violet;"><strong>Build and train a model</strong></mark></td><td><a href="/zingg/running-zingg/build-and-save-the-model.md">Build and Save the Model</a></td><td></td><td>Configure your data, label training pairs, and build a model for your dataset.</td></tr><tr><td><mark style="color:violet;background-color:violet;"><strong>Interpret and tune results</strong></mark></td><td><a href="/zingg/tuning/configure-field-standardization.md">Configure Field Standardization</a></td><td></td><td>Understand your match output, set thresholds, and improve accuracy.</td></tr><tr><td><mark style="color:violet;background-color:violet;"><strong>References</strong></mark></td><td><a href="/zingg/reference/configuration-schema.md">Configuration Schema</a></td><td></td><td>Configuration schema, CLI commands, runtime properties, and FAQ.</td></tr></tbody></table>

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