> ## Documentation Index
> Fetch the complete documentation index at: https://docs.shovels.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# How Shovels Verifies Data Accuracy

> Learn about the methods Shovels uses to verify data accuracy, including address standardization and contractor validation.

Shovels verifies data accuracy through multiple external sources to ensure reliability.

## Address Verification

For address standardization, we cross-reference details using **four different address sources**:

* National Address Dataset from the US Census
* Open Address dataset
* Simple Maps
* ESRI

This multi-source approach ensures:

* Consistent formatting
* Accurate geocoding
* Reliable location identification

## Contractor Verification

We match contractor information against:

* **Publicly available state license files**
* **Business registration records**

This ensures contractors in our database are:

* Properly licensed
* Legitimate operators in their respective fields

## Data Labeling Process

For permit classification, we employ a rigorous annotation process:

* Multiple independent annotators label each record
* Manual review resolves divergent responses
* Validation sample size is 1-5% of overall data

<Info>
  Our approach has achieved **98% accuracy** in classifications, validated by construction industry experts.
</Info>

## Golden Dataset Methodology

Annotators independently solve tasks rather than validating model outputs. This:

* Prevents annotator bias
* Creates a "golden dataset" of correct answers
* Enables benchmarking of new model outputs without fresh validation

## Related Articles

* [Data labeling process](/docs/knowledge-base/data/quality/labeling-process)
* [Data sources](/docs/knowledge-base/data/quality/data-sources)