Update AI Site

Update an AI website with content changes from detect-changes API. Uses THREE parallel Gemini calls (same pattern as onboarding):

Update llms.txt
Update homepage + Q&A pages
Update data.json (Schema.org)

Batch 1a: This endpoint runs as part of Batch 1a along with detect-changes. Product discovery (Batch 1b) is completely decoupled and runs in parallel.

The update flow:

detect-changes - Find what changed
update-ai-site (this endpoint) - Update the AI website

How It Works

Fetch current AI site files (llms.txt, homepage HTML, data.json)
Run 3 PARALLEL Gemini calls:
   - llms.txt: "Here's current + changes, update if needed"
   - Q&A pages: "Here's current + changes, update if needed"
   - data.json: "Here's current + changes, update if needed"
Check if any product source URLs changed → regenerate affected product llms
Generate files via generate_ai_site (only new + changed replica pages)
Deploy to Vercel (unless skip_deploy=True)
Store updated site_map + page_hashes
Return changed URLs for IndexNow submission (+ files if skip_deploy=True)

Product LLMs Regeneration

When pages change, this endpoint checks if any existing products have product_source_urls that overlap with the changed URLs. If so, those product llms files are regenerated.

Products in DB:
- Sandals: product_source_urls = ["/products/sandals", "/categories/footwear"]
- Boots: product_source_urls = ["/products/boots", "/winter-collection"]

Changed URLs from detect_changes:
- ["/products/sandals", "/about"]

Result:
- Sandals /llms/sandals.txt → REGENERATE (overlap: /products/sandals)
- Boots /llms/boots.txt → SKIP (no overlap)

Efficient updates: Product llms files are only regenerated if their source content changed. This scales well for sites with hundreds of products.

Partial content limitation: When regenerating product llms, we only use content from the changed pages. If a product has 3 source URLs and only 1 changed, the regenerated file will be based on that 1 page’s content. For a complete regeneration with all source content, use regenerate-fresh-website.

Request Body

Takes the output from detect-changes directly:

Field	Type	Required	Description
`business_id`	string	Yes	Clerk org ID
`new_pages`	array	Yes	Pages that didn’t exist before (have markdown)
`changed_pages`	array	Yes	Pages with content changes (have markdown)
`removed_urls`	array	Yes	URLs that no longer exist
`unchanged_pages`	array	Yes	Unchanged pages (NO markdown)
`updated_site_map`	array	Yes	Current URL list
`updated_hashes`	object	Yes	Current hash map
`business_info`	object	Yes	Business entity and AI site info
`skip_deploy`	boolean	No	If true, return files instead of deploying (default: false)

Key difference from old API: unchanged_pages replaces all_pages. Unchanged pages don’t have markdown because they weren’t batch scraped.

Response

{
  "status": "success",
  "files_updated": 42,
  "product_llms_regenerated": 2,
  "deployment_url": "https://example.searchcompany.dev",
  "project_name": "example-searchcompany-dev",
  "source_url": "https://example.com",
  "changed_urls": [
    "https://example.com/new-page",
    "https://example.com/about"
  ],
  "files": [...]  // Only present if skip_deploy=true
}

The changed_urls array should be submitted to IndexNow after deployment.

skip_deploy mode: When skip_deploy=True, the endpoint returns generated files in the response instead of deploying. This is used by the cron orchestrator to collect files from Batch 1a and 1b, then perform a single combined deployment.

Three Parallel Gemini Calls

Each call receives the current file + changes and decides what to update:

1. Update llms.txt

Current llms.txt: [current content]
Changes: [new/changed/removed pages]
Task: Update if needed, preserve AI Article sections

2. Update Q&A Pages

Current homepage HTML: [current structure]
Changes: [new/changed/removed pages]
Task: Update homepage + Q&A pages if needed, preserve AI Article sections

3. Update data.json

Current Schema.org: [current JSON-LD]
Changes: [new/changed/removed pages]
Task: Update if needed

AI Article sections are preserved - The Gemini prompts explicitly instruct NOT to modify any AI Article sections, as those are managed separately by the AI articles cron.

Database Updates

ai_sites.page_hashes - Updated hash map
ai_sites.site_map - Updated URL list
ai_sites.last_content_check_at - Timestamp

External API Calls

Gemini 3 Flash Preview (x3 parallel) - Content updates
Gemini 3 Flash Preview (per affected product) - Product llms regeneration
Vercel API - Deployment (unless skip_deploy=True)

Getting Started

Website

Onboarding

Cron

Your Current Setup

Explore

Settings - Toggle

Settings - Business

Settings - Team

Settings - Billing

Settings - Domain

Webhooks

Health

Manual Trigger

How It Works

Product LLMs Regeneration

Request Body

Response

Three Parallel Gemini Calls

1. Update llms.txt

2. Update Q&A Pages

3. Update data.json

Database Updates

External API Calls

​How It Works

​Product LLMs Regeneration

​Request Body

​Response

​Three Parallel Gemini Calls

​1. Update llms.txt

​2. Update Q&A Pages

​3. Update data.json

​Database Updates

​External API Calls

How It Works

Product LLMs Regeneration

Request Body

Response

Three Parallel Gemini Calls

1. Update llms.txt

2. Update Q&A Pages

3. Update data.json

Database Updates

External API Calls