ReCrawl AI + OTOs

If you use search apps that are driven by AI, you've probably asked yourself at least once, “Why is my AI search giving me old results?” Most of the time, the answer comes down to how fresh the index is, which is where ReCrawl AI comes in.

ReCrawl AI is a feature in Google Vertex AI Search that lets you re-crawl certain website URLs either by hand or automatically using the recrawlUris method. This keeps your AI search files up to date. But the word also has a second, wider meaning: the general practice of AI-driven recrawling that SEO experts, AI application developers, and product teams need more and more to make search experiences that are reliable and up to date.

At ReCrawl AI, we focus on just that: giving you the tools, software, and expert advice you need to keep AI search systems' indexes up to date.

This guide covers:

• A precise definition of ReCrawl AI and the recrawlUris mechanism
• How Google Vertex AI Search handles automatic and manual recrawling
• A step-by-step implementation walkthrough with real code examples
• Practical use cases across e-commerce, SaaS, and enterprise environments
• Quotas, technical limits, and a comparison with alternative indexing tools
• Answers to the most common questions teams ask before getting started

To understand what ReCrawl AI really is and how to use it safely, we first need a precise definition.

What Is ReCrawl AI? (Straightforward Definition)

ReCrawl AI refers to the ability in Google Vertex AI Search to manually or automatically re-crawl specific website URLs via the recrawlUris method, so that AI-powered search indexes stay fresh and return accurate results.

To be clear from the start, “ReCrawl AI” is not a separate Google product with its own name. This is a supported feature in Vertex AI Search's website indexing system—the way you can tell the platform, “This URL has changed, go fetch it again.” The difference is important because developers and SEO experts sometimes look for a separate tool that isn't a named product in Google Cloud.

Here is what the concept actually covers:

• Targeted URL refresh, You supply a specific list of URLs that have changed; Vertex re-processes those pages inside your data store.
• API-driven control, The recrawlUris method gives you programmatic access, which means you can automate it inside deployment pipelines or CMS workflows.
• Index accuracy for AI apps, Whether you are running an AI chatbot, a document search engine, or an enterprise knowledge portal, recrawl is the mechanism that keeps your underlying index truthful.
• Scoped to your data store, Recrawl only affects URLs already in scope of your configured website data store; it does not reach out and crawl the broader web.

The ReCrawl AI brand is in the middle of this area. It gives teams the tools, software, and technical advice they need to create and keep up-to-date new files that are ready for AI search.

How ReCrawl AI Works in Google Vertex AI Search

You won't have to guess when things go wrong if you know how the basic process works. This is the whole picture. We will talk about how Vertex AI Search creates its first index, the automatic refresh cycles, and the planned human recrawl API.

Overview of Vertex AI Search Website Indexing

Google Vertex AI Search puts information that has been indexed into structures known as “data stores.” A data store is like a special box that keeps a copy of your website's content in a way that Vertex AI's search and generation tools can read. You also set up an engine, which determines the search experience your app gives users in the end.

There is a certain order to setting up a website data store. You register your name, make sure you own it, and make sure that your server or robots.txt doesn't block the Vertex AI crawler. Then, Vertex gets your pages and indexes them. Following that, the platform will have a working copy of your site's information that AI-powered apps can use.

Think about an online store that has a 50,000-page collection of products. After the first crawl, they set up a website data store and point it at their domain. Then, their AI shopping helper can answer questions about product specs, availability, and price. The basis is that first crawl. After that, whether it's automatic or done by hand, everything is about making sure that base is correct.

This helps you understand why recrawl control is necessary for sites that are dynamic and change often.

Automatic Recrawl: How Vertex Keeps Data Fresh by Default

When the first index is made, Vertex AI Search doesn't just stop working on it. The platform will automatically and best-effort review URLs, which means it will re-fetch pages from time to time to find and include changes without you having to do anything.

Anyway, the word “best-effort” needs to be looked at closely. Vertex doesn't let you choose how often it crawls again. It doesn't promise a plan. The actual refresh rate relies on things like the size of your site, the rate of change that the crawler sees, health signals from the crawler, and the amount of space that is available for your project.

Automatic recrawl is all that's needed for many sites. If a business blog only posts two or three times a week and the content doesn't change very often, Vertex's background refresh will keep the index pretty up to date. In that case, it's fine to wait a few extra days for a new story to show up in AI search.

The problem shows up on changing sites. If you change the prices of your products three times a day or if your documentation team pushes changes after every sprint release, the automatic cycle will take too long. That time between when you change your content and when Vertex picks it up is where AI search results that are out of date come from. Manual recrawl is there to fill that gap whenever it's needed.

Manual Recrawl via recrawlUris: Targeted URL Refresh

You can choose which URLs to re-crawl and when to do it with the recrawlUris method. The process is simple: you make a list of URLs that have changed and send it to the Vertex AI Search API. The platform then plans an ordered crawl of those pages in your data store.

A few constraints govern how this works in practice:

• Up to 10,000 URLs per call, Each recrawlUris request accommodates a batch of up to 10,000 full URLs. No wildcard patterns; every URL must be specified explicitly.
• Up to 20 calls per day per project, This translates to a theoretical ceiling of around 200,000 URL refreshes per day per project, if every call uses maximum capacity.
• “Best effort” execution, The API prioritizes your submitted URLs over the background crawl queue, but it does not guarantee a specific time window for completion.
• Data store scope only, Recrawl operates within the boundaries of your configured data store. You cannot use it to index URLs from outside your registered domain.

In simple terms, the steps go like this: you find a change in the content; you collect the URLs that are affected; you send them via recrawlUris; Vertex re-crawls and updates the index; and then your AI search application starts returning the new data. When that cycle is automated, it's the real-world meaning of AI-driven indexing freshness.

Operations & Status: How Recrawl Results Are Reported

The API doesn't give you a success or loss message right away when you call recrawlUris. It instead gives you a long-running process resource, which you can use to see how the recrawl is going over time.

You can check on that action by calling operations.get, which gives you a status object with a few important fields:

• done, A boolean indicating whether the operation has completed.
• response.successCount, The number of URLs that were successfully re-crawled.
• response.failureCount, The number of URLs the crawler could not process.
• error, A global error field that fires if the operation itself failed (distinct from individual URL failures).

A business can run operations for up to 24 hours before they time out. This happens when there are a lot of batches, so it's not a sign that something is wrong.

In this real-life example, you send in 10,000 URLs after the prices on the whole site changed. After about two hours, the polls show that 9,750 did well and 250 did not. It turns out that the failed URLs were product pages that gave 404 errors because the stock had been wiped out. You can directly use that diagnostic data to figure out which pages need work before you re-queue them.

Pricing Plans

ReCrawl AI Standard – $77

• Commercial license included for client work
• Crawl content using ChatGPT AI engine
• 25 credits with 1 URL = 1 credit system
• Access to future updates and new features
• Includes support, tutorials, and bonus software

ReCrawl AI Max – $97

• Commercial license with expanded AI capabilities
• Crawl using ChatGPT, Gemini, and Anthropic engines
• 50 credits for increased crawling capacity
• Access to all future updates and feature releases
• Includes full support, tutorials, and bonus tools

Step-by-Step Guide: Implementing ReCrawl AI in Your Vertex AI Project

This section gives you a working implementation path. Whether you are a developer integrating recrawl into a CI/CD pipeline or a technical SEO building a scheduled refresh workflow, these steps apply.

Prerequisites: Setting Up for ReCrawl AI

Before you make your first recrawlUris call, confirm the following are in place:

• Active Google Cloud project with billing configured.
• Vertex AI Search API enabled for the project.
• Website indexing data store created, with domain verification complete and the Vertex AI crawler permitted in your robots.txt.
• IAM permissions that allow the calling identity (user account or service account) to invoke Vertex AI Search APIs.
• HTTP client or SDK, curl, the Python google-cloud-discoveryengine library, or the Node.js equivalent all work.

In enterprise environments, a dedicated service account with narrowly scoped permissions is the standard approach. If your site uses IP allowlists or bot-blocking logic, confirm that Google's Vertex AI crawler user-agent is explicitly permitted, otherwise your recrawl requests will register as failures even when the API call itself succeeds.

Building a Recrawl Request: JSON & API Endpoint

The recrawlUris request uses a POST method against the Vertex AI Search REST API. The JSON payload structure looks like this:

JSON

{

  “parent”: “projects/PROJECT_ID/locations/global/collections/default_collection/dataStores/DATASTORE_ID/engines/ENGINE_ID”,

  “recrawlUris”: {

    “uris”: [

      “https://example.com/updated-page1”,

      “https://example.com/updated-page2”

    ]

  }

}

Breaking down the key fields:

• parent, The full resource path identifying your Google Cloud project, data store, and engine. Replace PROJECT_ID, DATASTORE_ID, and ENGINE_ID with your actual values.
• uris, An array of fully qualified URLs you want re-crawled. Relative paths and wildcards are not accepted.

When your changed URL count exceeds 10,000, split the list into multiple batches and send them as separate API calls, staying within the 20-calls-per-day quota per project. Automating this batching logic inside your deployment script is a common pattern for large-scale sites.

Example: Triggering Recrawl with cURL or CLI

Once you have your JSON payload ready, triggering the recrawl from the command line is a single call. Here is a representative curl example:

Bash

curl -X POST \

  -H “Authorization: Bearer $(gcloud auth print-access-token)” \

  -H “Content-Type: application/json” \

  -d ‘{

    “recrawlUris”: {

      “uris”: [

        “https://example.com/updated-page1”,

        “https://example.com/updated-page2”

      ]

    }

  }' \

  “https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID/locations/global/collections/default_collection/dataStores/DATASTORE_ID/siteSearchEngine:recrawlUris”

Authentication works via a short-lived access token issued by the gcloud CLI. In production, service account credentials managed through Application Default Credentials (ADC) replace this pattern.

A successful call returns an operation name that looks like this:

JSON

{

  “name”: “projects/PROJECT_ID/locations/global/collections/default_collection/dataStores/DATASTORE_ID/operations/recrawl-OPERATION_ID”

}

Store that operation name. You will need it to monitor progress.

Monitoring Recrawl Operations: Checking Status & Counts

Poll the operation using a GET request to the operations endpoint:

Bash

curl -H “Authorization: Bearer $(gcloud auth print-access-token)” \

  “https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID/locations/global/collections/default_collection/dataStores/DATASTORE_ID/operations/recrawl-OPERATION_ID”

A completed operation returns a response similar to this:

JSON

{

  “name”: “projects/…/operations/recrawl-OPERATION_ID”,

  “done”: true,

  “response”: {

    “successCount”: “9950”,

    “failureCount”: “50”

  }

}

• Poll every 5–10 minutes for small batches; every 30–60 minutes for large ones.
• Operations time out at approximately 24 hours, if done is still false after that window, assume the operation expired and re-submit the batch.
• If a global error field appears instead of response, the operation itself failed, which typically indicates an API configuration or permission issue rather than individual URL problems.

Handling Errors & Failed URLs

Individual URL failures are normal and expected. The key is acting on them systematically rather than ignoring the failureCount.

The most common causes include:

• 404 responses, The page was removed or the URL changed after you submitted the batch.
• 5xx server errors, Your origin server returned an error during the crawler's fetch attempt.
• robots.txt blocking, A recent robots.txt change inadvertently disallowed the Vertex AI crawler.
• Redirect loops or timeouts, Slow or misconfigured redirects prevent the crawler from reaching the final page.

The recommended remediation cycle: export the list of failed URLs from your operation response → diagnose and fix the underlying issue on your server or configuration → re-submit only the corrected URLs in a new recrawlUris call.

Here is a practical example: a product page starts returning a 500 error because a back-end inventory service went down during a deployment. The recrawl marks it as failed. After the service is restored, you re-queue that URL alone, the fix is targeted, quota-efficient, and traceable.

ReCrawl AI Use Cases & Real-World Scenarios

The best way to see how useful ReCrawl AI is is to look at specific cases where old index data has a direct effect on business results.

E-Commerce: Keeping Prices, Stock & Promotions Accurate

Think about an online store that is having a flash sale. Prices go down, stock counters are updated every few minutes, and ads change every hour. A buying assistant AI based on Vertex AI Search might give the price from yesterday or say an item is available when it is actually sold out if it doesn't have a way to re-crawl.

The answer is to directly connect events like price changes, product updates, inventory thresholds, and promotional activations to a crawl queue. When an event occurs, the affected product page URLs are grouped together and sent through recrawlUris every hour or on an event-driven schedule, making sure that the daily limit is not exceeded.

The measurable result is a decrease in “price mismatch” support tickets and a more reliable AI shopping experience. These effects build over time as users come to believe the assistant's answers.

SaaS & Documentation: Reflecting Rapid Product Changes

SaaS teams get things done quickly. With a weekly release cycle, documentation pages for features, API links, and how to get started are always being updated. When an AI support chat is based on a Vertex AI Search index made from those documents, an out-of-date index means wrong answers, and wrong answers lead to support being escalated.

The method that works here is to make a recrawlUris call for the pages that have changed after every documentation deployment. Focus on articles and API reference pages that get a lot of traffic, as those are where most help questions come from. The end result is an AI helper that shows how the product is now, not how it was two sprints ago.

Internal Knowledge Bases & Enterprise Portals

Intranets and knowledge portals are used by big businesses to keep their workers updated. If an internal AI helper that is built on a Vertex AI Search data store that indexes those pages shows policy information that is out of date, it could lead to confusion or a risk of not following the rules.

ReCrawl AI works well with the governance process. When an HR policy changes, a compliance document is updated, or an emergency message is sent, the team that owns that URL instantly does a recrawl for it. The AI assistant shows the update in the operation window instead of the next planned crawl cycle, which might not happen for days.

AI-Powered Customer Support & Chatbots

AI help bots are only as good as the data they are based on. If a chatbot powered by Vertex AI Search looks at a FAQ page that was last updated three weeks ago, it will surely give you answers that are out of date. The user goes to a real person. Resolution drops after the first touch. Support costs go up.

To fix this, recrawl needs to be added to the process of publishing material. When the content team (or an automatic trigger in the CMS) makes a big change to a FAQ or troubleshooting guide, they put the new URLs in a queue to be crawled again. The next thing the bot says about that subject is based on new knowledge.

Now the next part of the story is ready. I used the style you asked for, making the necessary changes to the table, headers, and punctuation.

Limits, Quotas & Technical Constraints of ReCrawl AI

Before building a recrawl strategy, understanding the hard limits saves you from designing a system that hits a wall in production.

Limit Type	Value (Typical)	Notes
URLs per recrawlUris call	Up to 10,000	Full URLs only, no wildcards or URL patterns
Calls per day per project	Up to 20	Plan batching logic around this ceiling
Operation timeout window	~24 hours	Long-running operation, poll done status
Maximum URLs per day (theoretical)	~200,000	Assumes all 20 calls use full 10,000-URL capacity

A few things to keep in mind about these numbers. First, they can change because Google Cloud changes service limits all the time. Also, the values you see in your project console are more important than anything written by a third party, like this piece. Before committing to a production design, you should always check it against the official Cloud console.

Second, the number of 200,000 URLs per day is based on the idea of perfect batch packing. In real life, most recrawls happen in much smaller groups when real content changes, so the daily quota rarely becomes an issue, unless you run a very large site that gets updated often, like a news site or a marketplace with millions of entries.

If your site's update volume regularly gets close to these limits, you should set up a priority-based queuing system so that the pages that have the most traffic and business effect are re-crawled first, instead of treating all changed URLs the same.

ReCrawl AI vs. Alternative Recrawl & Indexing Tools

There are other ways to control how web information is indexed besides ReCrawl AI. Knowing how it works with other tools helps you put together the best stack for your needs.

Google Search Console Recrawl vs. Vertex ReCrawl AI

These two mechanisms are frequently confused, but they serve entirely different purposes and target entirely different indexes.

Dimension	Google Search Console	Vertex AI ReCrawl AI
Target index	Google organic search	Vertex AI Search (your app's index)
Interface	Web UI (URL Inspection tool)	REST API (recrawlUris method)
Scale	Individual URLs, manual submission	Up to 10,000 URLs per API call
Primary user	SEO specialist, webmaster	Developer, platform engineer
Use case	Improve organic ranking visibility	Maintain AI app search freshness

The easiest way to understand this is this: a content marketer asks Search Console to crawl a new blog post so that it shows up in Google Search results. After a product update, a developer uses recrawlUris to bring up to date a help article in the company's AI chatbot index. Both of these things involve “recrawling,” but they use completely different systems.

A lot of groups should do both of these things. They are layers that work together, not layers that compete with each other.

IndexNow & Other Push-Based Indexing Protocols

IndexNow is an open protocol that lets website owners tell participating search engines, mostly Bing, Yandex, and others that material has changed and needs to be crawled again. It's a quick and easy way to add new content without having to wait for search engine bots to find it on their own time.

Dimension	IndexNow	Vertex AI ReCrawl AI
Target engines	Bing, Yandex, other participants	Vertex AI Search (Google Cloud)
Protocol type	Open standard, HTTP push	Proprietary Google Cloud API
Scope	Web search rankings	Application-layer search indexes
Authentication	API key-based	Google Cloud IAM
Use case	News freshness, SEO visibility	AI app grounding data

In real life, IndexNow is different because it aims to get your information into web searches faster for SEO reasons. ReCrawl AI is meant to keep your Vertex-powered AI programs correct. Both work well for different groups of people, and it makes sense for a site that cares about both organic search exposure and AI search accuracy to use both at the same time.

For example, a big news organization might use IndexNow to let Bing know about breaking news stories and recrawlUris to keep their internal editing AI assistant's knowledge base up to date.

AI Crawlers & Data Extraction Tools (e.g., Crawl4AI)

Tools like Crawl4AI are in a whole different group. They are crawling and extraction tools that can be set up in different ways. Their main purpose is to collect content from websites and organize it into datasets so that they can be used to train machine learning models, build analytics pipelines, or do content audits.

Dimension	AI Crawlers (e.g., Crawl4AI)	Vertex AI ReCrawl AI
Primary output	Structured dataset / raw content	Updated production search index
Target audience	Data scientists, ML engineers	App developers, platform engineers
Production index update	No (requires separate pipeline)	Yes (directly updates the data store)
Use case	Model training, competitive research	Live AI app freshness

How it works is what makes it different. An AI crawler gives you information. When you run recrawlUris, it gives you an updated index that your production program can use right away. They are not the same thing.

A group of data scientists could use Crawl4AI to gather information about a competitor's products in order to create a dataset for price research. On a separate note, the tech team uses recrawlUris to keep their product catalog index up to date in the AI search experience that customers see. With both tools in use, very different goals are being met.

Supplemental FAQs & Conceptual Questions About ReCrawl AI

Is ReCrawl AI an official Google product name?

Not at all. Google does not sell a product called “ReCrawl AI.” The recrawlUris method in the Vertex AI Search API for website indexing data stores is described in the Google Cloud developer docs as the mechanism behind “ReCrawl AI.” The word “ReCrawl AI” is both used to describe this feature and as the name of the brand you are reading about right now, which makes tools and guides based on that documented feature.

Does ReCrawl AI affect my rankings in Google Search?

Not at all. Google's original web search index is not the same as Vertex AI Search. When you call recrawlUris, it only changes the data store inside your application. It doesn't change how Google's crawlers work on your pages for google.com search results. Link Inspection in Google Search Console or IndexNow (for engines other than Google) is the right tool to use to change how quickly organic search results are indexed.

What are the main components involved in a ReCrawl AI workflow?

A complete recrawl workflow typically involves five layers working in sequence:

• Content source, Your website or CMS, where pages are created and updated.
• Change detection, The logic (event triggers, deployment hooks, or scheduled diffs) that identifies which URLs have changed.
• Recrawl API calls, The recrawlUris requests that submit changed URLs to Vertex AI Search.
• Monitoring and logging, Operation polling, success/failure tracking, and alerting for failed URLs.
• AI application, The chatbot, search UI, or agent that ultimately queries the refreshed index and delivers answers to users.

How is ReCrawl AI different from simply crawling more often?

Two problems happen when you increase the crawl frequency without thinking about it: it makes your origin server work harder than it needs to, and it doesn't promise that the right pages get refreshed at the right time. Instead, ReCrawl AI lets you tell it exactly which URLs changed and when, so it can focus its crawling power on the pages that really need it. That level of detail is what makes tailored, API-driven recrawl different from brute-force crawl scheduling.

Can I use ReCrawl AI for a brand-new site with no initial index?

Not right away. The recrawlUris method works with a website data store that is already set up. First, you need to set up everything: make the data store, make sure you own the domain, let the Vertex AI crawler in, and do the first full crawl. Once that base search is set, you can use recrawl to speed up updates for certain pages as your content changes.

Is ReCrawl AI free to use?

The recrawlUris API call is part of the Vertex AI Search service, and it's priced the same way Google Cloud does everything else. The costs depend on how your data store is set up, how many queries you run, and which Vertex AI Search plan your project uses. Although recrawl calls are free, the service is not, and how it is used is based on the price and quotas that are in place for your Google Cloud project. Before making a cost model, you should always check the Google Cloud interface for the most up-to-date prices.

What types of sites benefit least from ReCrawl AI?

Some sites simply do not have a strong case for implementing programmatic recrawl. The main categories where the benefit is minimal:

• Small static sites, A five-page brochure site that changes once a month will be well-served by automatic background recrawl.
• Personal blogs with low update frequency, Infrequent posts and stable content mean the automatic cycle is more than adequate.
• Micro-sites not powering an AI application, If there is no Vertex AI Search-powered application drawing from the site's content, recrawl does not apply.

If none of your users interact with an AI search or conversational interface backed by Vertex AI Search, recrawl management is not relevant to your stack.

Should I build my own crawler instead of using ReCrawl AI?

The crawler should do what you want it to do. Making your own crawler gives you complete control over the depth of the crawl, the rules for content extraction, and the transformation of the data. This is useful for creating training datasets or running your own analytics. A custom crawler, on the other hand, does not naturally work with Vertex AI Search's output index. To add to and change that index, you would still need a different pipeline, which adds a lot of extra work to the engineering side.

When you want to keep the data store for Vertex AI Search up to date, the controlled recrawlUris API is the best way to go. If you want to collect data, analyze it, or train a model, you can make your own crawler. If you want to keep the production index up to date for a live AI app, use ReCrawl AI.