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

# Doc Analyst

### Overview

EmpowerGPT’s **Doc Analyst** is a specialized GenAI-powered app designed to analyze the enterprise documents through user-defined, purpose-built questionnaires. Whether working with reports, contracts, policies, financial records, or any other corporate documents, Doc Analyst streamlines the extraction of meaningful insights—making it a core tool for modern organizations.

<figure><img src="/files/7g4gDaUoKfeHgoPOEtUV" alt=""><figcaption></figcaption></figure>

### **Key Capabilities**

#### **Precise Answer Extraction**

Doc Analyst generates highly accurate, reference-backed answers to custom questions by interpreting the content of selected documents.

#### **Purpose-Specific Questionnaires**

Users can design tailored questionnaires aligned with specific business workflows—such as compliance checks, contract reviews, audit preparation, policy validation, or document summarization.

#### **Effortless Knowledge Dataset Integration**

Datasets can be created by importing files or folders directly from the EmpowerGPT app, enabling seamless access to enterprise documents during analysis.

#### **Comprehensive Analysis Capabilities**

The tool supports detailed, question-by-question analysis, allowing users to review, regenerate, and refine results. Each response includes document references with page-level citations.

#### **On-the-Go Modification & Regeneration**

After reviewing results, users can edit questions and automatically regenerate updated responses—without rerunning the entire analysis.

#### **Active Collaboration with Team Members**

Teams can work together by reviewing shared analyses, editing questionnaires, and collectively refining insights to ensure completeness and accuracy.


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# Agent Instructions
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## Querying This Documentation
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Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
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```

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