# AI Crawl Analysis: Iris Ibekwe’s Vision for Smarter Government Web Audits

 

 

Date: Friday, September 5, 2025

 By  [CivicActions](/civicactions)

*Iris Ibekwe, Associate Director of Front End Development*

*Iris Ibekwe, Associate Director of Front End Development at CivicActions, is bringing innovation to the newly launched* [***Open Python Directory***](https://github.com/CivicActions/open-python-directory?tab=readme-ov-file)[ ](https://github.com/CivicActions/open-python-directory?tab=readme-ov-file)*with her contribution of* [***AI Crawl Analysis***](https://github.com/CivicActions/ai-crawl-analysis)*. The AI-powered tool is designed to help public sector teams quickly understand and streamline the complex structure of their web content, making the process of digital consolidation faster, clearer, and more effective.*

*In this interview, Iris shares how the tool addresses long-standing challenges in web audits, how it reflects CivicActions’ values of openness and collaboration, and what impact she hopes it will have for agencies and partners.*

## 1. What problem does this tool solve for public-sector teams, and why does it matter?

Public sector teams often manage multiple websites for various purposes. Over time, as these websites age, there is a need to consolidate their content to make it easier for the public to access relevant and up-to-date information. To do this, public sector teams typically undergo an extensive auditing process to gather the insights needed to make informed decisions about which content to highlight or archive. This process is usually slow and cumbersome, especially when dealing with hundreds or thousands of pages across different websites. Part of the auditing involves running site crawls to gather more information about a website. While traditional site crawls help determine a website’s size and valid URLs, they don’t provide additional valuable context about content types and the complex content structures that can complicate migrations.

The AI-enhanced site auditing process uses AI prompts to gather additional context about a site’s pages during site crawls and also performs further analysis on the extracted data to provide relevant insights. Since the process is much faster than manual site audits, teams can analyze and draw parallels between multiple websites more quickly. The tool helps teams make better decisions about creating the best information architecture to present their previously fractured content in a streamlined and non-duplicative way.

## 2. How do you see this lining up with CivicActions’ values of openness, collaboration, and reuse?

The crawl analysis code is an open-source, reusable toolkit stored on a public GitHub repository. This means that anyone can see a public history of how it was built, how it functions, and learn how to use it. Also, other individuals or teams can submit issues, suggest features, or fork the tool to customize it. This encourages collaboration and improves the quality of the codebase. Because it is built with Python and open-source tools like Streamlit, it avoids vendor lock-in and can run on any server configured for Python scripts. The code is organized modularly with reusable blocks for different tasks. This encourages reusability, allowing teams to run scripts individually or collectively, depending on their project needs.

## 3. What impact do you hope it has for agencies or partners working on consolidation?

My hope is that this tool gives public sector teams a head start on understanding the scope of their web properties. It should also provide faster, evidence-based insights about what content to highlight, merge, or archive, and help create a streamlined information architecture that delivers relevant content right at their audiences’ fingertips. Ultimately, I want it to transform the labor-intensive process of auditing and consolidating public sector websites into a quicker and more delightful experience for agencies and partners working on consolidation.

*Explore the* [*Open Python Directory*](https://github.com/CivicActions/open-python-directory?tab=readme-ov-file) *and try out* [*AI Crawl Analysis*](https://github.com/CivicActions/ai-crawl-analysis)*.*