> For the complete documentation index, see [llms.txt](https://factagora.gitbook.io/deepverify/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://factagora.gitbook.io/deepverify/getting-started/introduction.md).

# Introduction

The **DeepVerify API** is designed to help organizations verify the accuracy and validity of information across various domain-specific problems. By leveraging Factagora’s advanced knowledge graphs and governance models, DeepVerify enhances the reliability of automated systems, minimizes errors, and improves decision-making. Whether it’s verifying healthcare claims, assessing legal contracts, or analyzing ESG reports, DeepVerify ensures that AI outputs are based on factual and trusted data.

## Common use cases for the DeepVerify API 

* Fact-check any text for domain-specific problems 
* Generate verification reports for domain-specific problems 
* Judge your generative AI performance 

## Why DeepVerify API? 

DeepVerify stands apart from other verification tools because it goes beyond surface-level checks. It deeply traverses Factagora’s knowledge graphs to understand governance rules and conditions, delivering results with human-like accuracy. Here’s why you should choose DeepVerify:

* **Automated and accurate**: It reduces manual effort while delivering high accuracy in verification, especially for complex domains like healthcare and legal sectors. 
* **Scalable**: DeepVerify can handle large volumes of text and data, making it suitable for enterprise applications that need constant verification. 
* **Time-saving**: By automating the entire verification process, DeepVerify significantly cuts down on review time, allowing businesses to focus on critical tasks. 
* **Improves AI Governance**: With DeepVerify, companies can ensure their AI systems remain accurate and compliant, reducing the risk of errors and disinformation.
* **Disinformation Security**: Protect your organization from the risks of AI-generated disinformation by verifying the accuracy of content in real-time. 
* **Evaluate Your Generative AI**: Assess and verify the output of generative AI models to ensure they align with factual and trusted data, improving the overall performance and trustworthiness of your AI.