Guide17 Apr 2026

Automating AEO Reports via API: Templates Included

Learn to automate Answer Engine Optimization reports via API. Step-by-step guide with code templates for ChatGPT, Perplexity, and Gemini tracking.

KKevin14 minutes read

Why Manual AEO Reporting Is Bleeding Your Team's Time

You're tracking visibility across ChatGPT, Perplexity, Gemini, and Claude. Every week, someone pulls data from multiple dashboards, copies citations into a spreadsheet, and sends a report that's already stale by Friday. For agencies managing five clients, this is twenty hours of manual work. For in-house teams, it's a sprint tax that kills velocity.

Answer Engine Optimization (AEO) requires constant monitoring. AI search engines change their citation patterns weekly. Your content's visibility in Claude shifts independently from Perplexity. A brand that ranked in Gemini's top answers last month might have dropped to secondary citations this month, and you won't know until someone manually checks.

The fix: automate your AEO reporting pipeline using the SearchFit API. Instead of manual dashboards, you'll have scheduled reports that land in Slack, email, or your internal BI tool every morning. Your team gets real-time visibility into which AI platforms are citing your brand, which content is performing, and where traffic is leaking.

This guide walks you through building that automation from scratch. We'll cover API authentication, report scheduling, template customization, and integration patterns that work for agencies and enterprise teams.

Prerequisites: What You Need Before Starting

Before you begin automating AEO reports, confirm you have these in place:

API Access and Credentials

You'll need an active SearchFit account with API access enabled. Visit SearchFIT's main platform to set up your account if you haven't already. Once your account is active, generate your API key from the settings dashboard. Store this key securely, treat it like a database password. You'll use it to authenticate every request to the SearchFit API.

Development Environment

You'll need a basic development setup: Python 3.8+, Node.js 16+, or your preferred language for making HTTP requests. All code examples in this guide use Python and JavaScript, but the API patterns translate to any language. Install a package manager (pip for Python, npm for Node.js) and a code editor.

Scheduling Infrastructure

To run reports on a schedule, you need either a cron job (if you're self-hosting), a serverless function (AWS Lambda, Google Cloud Functions, Azure Functions), or a workflow automation platform like n8n or Zapier. If you're new to automation, Zapier is the gentlest entry point. If you're comfortable with code, Lambda or a cron job gives you more control.

Destination for Reports

Decide where your reports will land: Slack, email, Google Sheets, a BI tool like Looker Studio or Tableau, or your internal database. Each destination requires different setup (Slack webhooks, SMTP credentials, Google Sheets API keys). Have those credentials ready.

Brand Tracking Configuration

Before the API can report on your brand's visibility, you need to have configured your tracked brands and keywords in SearchFit. Set up your primary brand name, key product names, and high-intent keywords in SearchFIT's tracking dashboard. The API will pull data only for brands and keywords you've already configured.

Understanding the SearchFit API Structure

The SearchFit API is a REST API that exposes three core endpoints for AEO reporting:

The Brand Visibility Endpoint

This endpoint returns your brand's citation frequency across all tracked AI engines (ChatGPT, Perplexity, Gemini, Claude, Grok). It shows citation count, ranking position within answers, and citation velocity (whether mentions are increasing or decreasing). The response includes engine-specific breakdowns, so you can see that Perplexity cited you 47 times this week while Gemini cited you only 12 times.

The Content Performance Endpoint

This endpoint shows which pieces of your content are being cited by AI search engines. You'll see URL, title, citation count per engine, and the queries that triggered those citations. This is critical for understanding what content resonates with AI systems, often different from what ranks in traditional Google search.

The Citation Tracking Endpoint

This endpoint provides granular citation data: exact query, engine, position in the answer, citation text, and timestamp. Use this to detect when your brand is cited in answer context (product recommendations, comparisons, how-to guides) versus being ignored entirely.

All endpoints support filtering by date range, engine, and brand. All responses return JSON. All requests require your API key in the Authorization header.

Step 1: Generate and Secure Your API Key

Log into your SearchFit account and navigate to Settings > API & Integrations. Click Generate New API Key. You'll see a key that looks like this:

sk_live_51a2b3c4d5e6f7g8h9i0j1k2l3m4n5o6p

Store this key securely. Never commit it to version control. Never paste it in Slack or email. Use environment variables or a secrets manager:

For local development (Python):

Create a .env file in your project root:

SEARCHFIT_API_KEY=sk_live_51a2b3c4d5e6f7g8h9i0j1k2l3m4n5o6p

Load it in your script:

import os
from dotenv import load_dotenv

load_dotenv()
api_key = os.getenv('SEARCHFIT_API_KEY')

For production (Lambda, Cloud Functions):

Store the key in your cloud provider's secrets manager. AWS Secrets Manager, Google Secret Manager, and Azure Key Vault all work. Your function retrieves the key at runtime without exposing it in code.

For Zapier or n8n:

Use the platform's built-in credential storage. Zapier encrypts credentials and injects them into requests automatically. n8n has a similar credentials panel.

Test your key by making a simple request:

curl -H "Authorization: Bearer sk_live_51a2b3c4d5e6f7g8h9i0j1k2l3m4n5o6p" \
https://api.searchfit.ai/v1/brands

You should get a 200 response with your configured brands. If you get a 401, your key is invalid or expired.

Step 2: Fetch Brand Visibility Data

Now you'll pull your brand's visibility metrics from SearchFit. Here's a Python example:

import requests
import os
from datetime import datetime, timedelta

api_key = os.getenv('SEARCHFIT_API_KEY')
base_url = 'https://api.searchfit.ai/v1'

headers = {
'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json'
}

# Get visibility for the last 7 days
end_date = datetime.now().date()
start_date = end_date - timedelta(days=7)

params = {
'brand_id': 'your_brand_id',
'start_date': start_date.isoformat(),
'end_date': end_date.isoformat(),
'engines': 'chatgpt,perplexity,gemini,claude'
}

response = requests.get(
f'{base_url}/brands/visibility',
headers=headers,
params=params
)

if response.status_code == 200:
visibility_data = response.json()
print(visibility_data)
else:
print(f'Error: {response.status_code}')
print(response.text)

The response looks like this:

{
"brand_id": "your_brand_id",
"period": "2025-01-01 to 2025-01-07",
"total_citations": 342,
"engines": {
"chatgpt": {
"citations": 145,
"trend": "up",
"change_percent": 12.5
},
"perplexity": {
"citations": 98,
"trend": "flat",
"change_percent": 0
},
"gemini": {
"citations": 67,
"trend": "down",
"change_percent": -8.3
},
"claude": {
"citations": 32,
"trend": "up",
"change_percent": 25.0
}
}
}

Parse this data and store it. You'll format it into a report in the next step.

Step 3: Fetch Content Performance Data

Next, pull which of your content pieces are being cited. This shows you what's actually resonating with AI systems:

params = {
'brand_id': 'your_brand_id',
'start_date': start_date.isoformat(),
'end_date': end_date.isoformat(),
'limit': 50  # Top 50 cited pieces
}

response = requests.get(
f'{base_url}/content/performance',
headers=headers,
params=params
)

if response.status_code == 200:
content_data = response.json()
# Sort by total citations
sorted_content = sorted(
content_data['content'],
key=lambda x: x['total_citations'],
reverse=True
)

for item in sorted_content[:10]:
print(f"{item['title']} - {item['total_citations']} citations")
print(f"  ChatGPT: {item['engines']['chatgpt']}")
print(f"  Perplexity: {item['engines']['perplexity']}")
else:
print(f'Error: {response.status_code}')

This shows you which blog posts, product pages, and guides are being cited most frequently. If your "How to Choose a CRM" guide has 89 citations while your product overview page has 3, that's a signal to create more how-to and comparison content.

Step 4: Build Your Report Template

Now combine the visibility and content data into a structured report. Here's a template that works for email, Slack, and BI tools:

def generate_aeo_report(visibility_data, content_data, brand_name):
report = {
'title': f'AEO Report: {brand_name}',
'period': visibility_data['period'],
'summary': {
'total_citations': visibility_data['total_citations'],
'trend': calculate_trend(visibility_data),
'engines_cited': len([e for e in visibility_data['engines'] if visibility_data['engines'][e]['citations'] > 0])
},
'engine_breakdown': visibility_data['engines'],
'top_content': content_data['content'][:10],
'alerts': generate_alerts(visibility_data, content_data),
'recommendations': generate_recommendations(visibility_data, content_data)
}
return report

def calculate_trend(visibility_data):
positive = sum(1 for e in visibility_data['engines'].values() if e['trend'] == 'up')
negative = sum(1 for e in visibility_data['engines'].values() if e['trend'] == 'down')

if positive > negative:
return 'positive'
elif negative > positive:
return 'negative'
else:
return 'flat'

def generate_alerts(visibility_data, content_data):
alerts = []

# Alert if any engine dropped >20%
for engine, data in visibility_data['engines'].items():
if data['change_percent'] < -20:
alerts.append(f'{engine.capitalize()} citations dropped {abs(data["change_percent"])}%')

# Alert if no citations from an expected engine
for engine in ['chatgpt', 'perplexity', 'gemini']:
if visibility_data['engines'][engine]['citations'] == 0:
alerts.append(f'No citations detected in {engine.capitalize()}')

return alerts

def generate_recommendations(visibility_data, content_data):
recommendations = []

# Recommend content creation based on top-performing pieces
top_categories = {}
for item in content_data['content'][:20]:
category = item.get('category', 'general')
top_categories[category] = top_categories.get(category, 0) + item['total_citations']

best_category = max(top_categories, key=top_categories.get)
recommendations.append(f'Create more {best_category} content (your top-cited category)')

# Recommend optimization for underperforming engines
for engine, data in visibility_data['engines'].items():
if data['citations'] < 50:
recommendations.append(f'Optimize for {engine.capitalize()} (currently {data["citations"]} citations)')

return recommendations

This template generates structured data you can format into email, Slack messages, or JSON for your BI tool.

Step 5: Format for Different Destinations

Email Reports

Use a templating library to generate HTML emails:

from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
import smtplib

def send_email_report(report, recipient_email):
html_body = f"""
<html>
<body style="font-family: Arial, sans-serif;">
<h2>{report['title']}</h2>
<p>Period: {report['period']}</p>

<h3>Summary</h3>
<ul>
<li>Total Citations: {report['summary']['total_citations']}</li>
<li>Trend: {report['summary']['trend'].upper()}</li>
<li>Engines Citing You: {report['summary']['engines_cited']}/6</li>
</ul>

<h3>Engine Breakdown</h3>
<table style="border-collapse: collapse; width: 100%;">
<tr style="background-color: #f0f0f0;">
<th style="border: 1px solid #ddd; padding: 8px;">Engine</th>
<th style="border: 1px solid #ddd; padding: 8px;">Citations</th>
<th style="border: 1px solid #ddd; padding: 8px;">Change</th>
</tr>
"""

for engine, data in report['engine_breakdown'].items():
html_body += f"""
<tr>
<td style="border: 1px solid #ddd; padding: 8px;">{engine.capitalize()}</td>
<td style="border: 1px solid #ddd; padding: 8px;">{data['citations']}</td>
<td style="border: 1px solid #ddd; padding: 8px;">{data['change_percent']:+.1f}%</td>
</tr>
"""

html_body += """</table>"""

if report['alerts']:
html_body += "<h3>⚠️ Alerts</h3><ul>"
for alert in report['alerts']:
html_body += f"<li>{alert}</li>"
html_body += "</ul>"

html_body += "<h3>Top Cited Content</h3><ol>"
for item in report['top_content'][:5]:
html_body += f"<li><a href=\"{item['url']}\">{item['title']}</a> ({item['total_citations']} citations)</li>"
html_body += "</ol>"

html_body += "</body></html>"

# Send via SMTP
msg = MIMEMultipart('alternative')
msg['Subject'] = report['title']
msg['From'] = '[email protected]'
msg['To'] = recipient_email
msg.attach(MIMEText(html_body, 'html'))

with smtplib.SMTP('smtp.gmail.com', 587) as server:
server.starttls()
server.login('[email protected]', os.getenv('GMAIL_PASSWORD'))
server.send_message(msg)

Slack Reports

Format as a Slack Block Kit message:

import json
import requests

def send_slack_report(report, webhook_url):
# Build engine breakdown as fields
fields = []
for engine, data in report['engine_breakdown'].items():
trend_emoji = '📈' if data['trend'] == 'up' else '📉' if data['trend'] == 'down' else '➡️'
fields.append({
'type': 'mrkdwn',
'text': f"*{engine.capitalize()}*\n{data['citations']} citations {trend_emoji} {data['change_percent']:+.1f}%"
})

payload = {
'blocks': [
{
'type': 'header',
'text': {
'type': 'plain_text',
'text': f"📊 {report['title']}"
}
},
{
'type': 'section',
'fields': [
{
'type': 'mrkdwn',
'text': f"*Total Citations*\n{report['summary']['total_citations']}"
},
{
'type': 'mrkdwn',
'text': f"*Trend*\n{report['summary']['trend'].upper()}"
}
]
},
{
'type': 'section',
'fields': fields[:4]  # Limit to 4 fields per section
}
]
}

if report['alerts']:
payload['blocks'].append({
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': '⚠️ *Alerts*\n' + '\n'.join(f"• {a}" for a in report['alerts'])
}
})

# Add top content
top_content_text = '*Top Cited Content*\n'
for i, item in enumerate(report['top_content'][:5], 1):
top_content_text += f"{i}. <{item['url']}|{item['title']}> ({item['total_citations']} citations)\n"

payload['blocks'].append({
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': top_content_text
}
})

response = requests.post(webhook_url, json=payload)
return response.status_code == 200

Google Sheets Integration

Pipe data directly into a spreadsheet for live dashboards:

from google.oauth2.service_account import Credentials
from google.auth.transport.requests import Request
import gspread

def write_to_google_sheets(report, spreadsheet_id, worksheet_name):
# Authenticate using service account
creds = Credentials.from_service_account_file(
'service_account.json',
scopes=['https://www.googleapis.com/auth/spreadsheets']
)

client = gspread.authorize(creds)
spreadsheet = client.open_by_key(spreadsheet_id)
worksheet = spreadsheet.worksheet(worksheet_name)

# Clear existing data
worksheet.clear()

# Write headers
headers = ['Date', 'Engine', 'Citations', 'Trend', 'Change %']
worksheet.append_row(headers)

# Write engine data
from datetime import datetime
today = datetime.now().strftime('%Y-%m-%d')

for engine, data in report['engine_breakdown'].items():
row = [
today,
engine.capitalize(),
data['citations'],
data['trend'],
data['change_percent']
]
worksheet.append_row(row)

# Write top content to separate sheet
content_sheet = spreadsheet.worksheet('Top Content')
content_sheet.clear()

content_headers = ['Title', 'URL', 'Total Citations', 'ChatGPT', 'Perplexity', 'Gemini', 'Claude']
content_sheet.append_row(content_headers)

for item in report['top_content'][:20]:
row = [
item['title'],
item['url'],
item['total_citations'],
item['engines'].get('chatgpt', 0),
item['engines'].get('perplexity', 0),
item['engines'].get('gemini', 0),
item['engines'].get('claude', 0)
]
content_sheet.append_row(row)

Step 6: Automating Reports on a Schedule

Now automate the entire pipeline to run on a schedule. Choose your infrastructure:

Option A: AWS Lambda with CloudWatch Events

Create a Lambda function that runs the full report pipeline:

import json
import os
import requests
from datetime import datetime, timedelta

def lambda_handler(event, context):
api_key = os.environ['SEARCHFIT_API_KEY']

# Fetch data
visibility_data = fetch_visibility(api_key)
content_data = fetch_content(api_key)

# Generate report
report = generate_aeo_report(visibility_data, content_data, 'Your Brand')

# Send to all destinations
send_email_report(report, '[email protected]')
send_slack_report(report, os.environ['SLACK_WEBHOOK'])
write_to_google_sheets(report, os.environ['SHEET_ID'], 'Weekly Report')

return {
'statusCode': 200,
'body': json.dumps('Report generated and sent')
}

def fetch_visibility(api_key):
# Implementation from Step 2
pass

def fetch_content(api_key):
# Implementation from Step 3
pass

Deploy this function and create a CloudWatch Events rule to trigger it weekly:

aws events put-rule --name aeo-report-weekly --schedule-expression 'cron(0 8 ? * MON *)'
aws events put-targets --rule aeo-report-weekly --targets "Id"="1","Arn"="arn:aws:lambda:region:account:function:aeo-report"

This runs every Monday at 8 AM UTC.

Option B: n8n Workflow

If you prefer no-code automation, build an n8n workflow:

HTTP Request node → Call SearchFit API for visibility data
HTTP Request node → Call SearchFit API for content data
Code node → Combine data and generate report
Email node → Send email report
Webhook node → Send to Slack
Google Sheets node → Write to spreadsheet
Cron trigger → Set to run weekly

n8n's visual interface makes this approachable even for non-engineers. Learn more about automating AEO workflows in this comprehensive guide on AEO workflows using n8n and Zapier.

Option C: Zapier

For the simplest approach, use Zapier's pre-built connectors:

Trigger: Schedule (weekly)
Action: HTTP request to SearchFit API
Action: Format as email/Slack message
Action: Send email or post to Slack
Action: Append to Google Sheets

Zapier handles authentication and scheduling without code.

Step 7: Monitor and Iterate

Once your reports are running automatically, monitor their accuracy and usefulness:

Validate API Responses

Add error handling to catch API issues before they break your reports:

def fetch_with_retry(url, headers, params, max_retries=3):
for attempt in range(max_retries):
try:
response = requests.get(url, headers=headers, params=params, timeout=10)
if response.status_code == 200:
return response.json()
elif response.status_code == 429:  # Rate limited
time.sleep(2 ** attempt)  # Exponential backoff
continue
else:
raise Exception(f'API error: {response.status_code}')
except requests.exceptions.RequestException as e:
if attempt == max_retries - 1:
raise
time.sleep(2 ** attempt)

return None

Track Report Delivery

Log when reports are sent and to whom:

import logging

logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('aeo_reports.log'),
logging.StreamHandler()
]
)

logger = logging.getLogger(__name__)

logger.info(f'Report generated for period {report["period"]}')
logger.info(f'Total citations: {report["summary"]["total_citations"]}')
logger.info(f'Email sent to [email protected]')
logger.info(f'Slack message posted')

Refine Alert Thresholds

After a few weeks of reports, adjust your alert triggers based on what's actually important:

# Initial: alert on any >20% drop
# After data: alert only on >30% drop in ChatGPT (high volume)
# Alert on >15% drop in Claude (low volume, so smaller changes matter more)

def generate_alerts_v2(visibility_data):
alerts = []

thresholds = {
'chatgpt': -30,
'perplexity': -25,
'gemini': -20,
'claude': -15  # More sensitive to smaller engines
}

for engine, threshold in thresholds.items():
if engine in visibility_data['engines']:
change = visibility_data['engines'][engine]['change_percent']
if change < threshold:
alerts.append(f'{engine.capitalize()} dropped {abs(change):.1f}%')

return alerts

Integration with SearchFit's Analytics Dashboard

While you're automating reports, use SearchFIT's analytics platform for deeper investigation. The API powers your scheduled reports, but the dashboard lets your team explore data ad-hoc:

Drill into specific queries: See which searches triggered your citations
Compare engines side-by-side: Understand why Perplexity cites you more than Gemini
Analyze citation context: Is your brand cited in product recommendations, comparisons, or general advice?
Track content performance: See which pages get cited and in what context

Your automated reports flag trends. The dashboard lets you investigate root causes.

Pro Tips for Production Deployments

Implement Idempotency

If your report job runs twice, you don't want duplicate emails:

def generate_report_with_idempotency_key(brand_id, period_start, period_end):
idempotency_key = f"{brand_id}_{period_start}_{period_end}"

# Check if this report was already generated
if report_exists(idempotency_key):
logger.info(f'Report {idempotency_key} already generated, skipping')
return None

# Generate new report
report = generate_aeo_report(...)

# Store idempotency key
store_generated_report(idempotency_key, report)

return report

Use Batch Requests for Multiple Brands

If you're an agency managing 10+ brands, batch your API calls:

def generate_reports_for_all_brands(api_key, brand_ids):
reports = {}

# Fetch all visibility data in parallel
import concurrent.futures

with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
futures = {executor.submit(fetch_visibility, api_key, bid): bid for bid in brand_ids}

for future in concurrent.futures.as_completed(futures):
brand_id = futures[future]
try:
reports[brand_id] = future.result()
except Exception as e:
logger.error(f'Failed to fetch data for {brand_id}: {e}')

return reports

Archive Historical Reports

Store reports in a database or S3 to track trends over time:

import boto3
from datetime import datetime

def archive_report_to_s3(report, bucket_name, brand_id):
s3 = boto3.client('s3')

timestamp = datetime.now().isoformat()
key = f'aeo-reports/{brand_id}/{timestamp}.json'

s3.put_object(
Bucket=bucket_name,
Key=key,
Body=json.dumps(report),
ContentType='application/json'
)

logger.info(f'Report archived to s3://{bucket_name}/{key}')

Common Issues and Troubleshooting

"401 Unauthorized" Errors

Your API key is invalid, expired, or not being passed correctly. Verify:

The key is in your environment variables
The Authorization header format is exactly Bearer {key}
The key hasn't been rotated in your SearchFit account

"429 Too Many Requests"

You're hitting rate limits. The SearchFit API allows 100 requests per minute. Implement exponential backoff (wait 1s, then 2s, then 4s) before retrying.

Reports Are Empty

You haven't configured brands or keywords in SearchFit yet. Visit SearchFIT's tracking page and set up your tracked brands before running reports.

Slack Messages Aren't Formatting

Block Kit syntax changed. Validate your payload using Slack's Block Kit Builder before posting.

Google Sheets Authentication Fails

Your service account doesn't have access to the spreadsheet. Share the sheet with the service account email (looks like [email protected]) and give it Editor access.

Advanced: Multi-Client Agency Setup

If you're an agency managing AEO for multiple clients, extend this pattern to handle many brands:

def generate_reports_for_agency(api_key, client_configs):
"""
client_configs = [
{
'client_id': 'client_1',
'brand_id': 'brand_1',
'email': '[email protected]',
'slack_webhook': 'https://hooks.slack.com/...',
'sheet_id': 'google_sheet_id_1'
},
...
]
"""

for config in client_configs:
try:
# Fetch data
visibility = fetch_visibility(api_key, config['brand_id'])
content = fetch_content(api_key, config['brand_id'])

# Generate report
report = generate_aeo_report(visibility, content, config['client_id'])

# Send to client-specific destinations
if config.get('email'):
send_email_report(report, config['email'])

if config.get('slack_webhook'):
send_slack_report(report, config['slack_webhook'])

if config.get('sheet_id'):
write_to_google_sheets(report, config['sheet_id'], 'Weekly AEO')

logger.info(f'Report sent for {config["client_id"]}')

except Exception as e:
logger.error(f'Failed to generate report for {config["client_id"]}: {e}')
# Send error notification to agency
send_error_alert(config, e)

This pattern scales to dozens of clients without code changes.

Comparing API Automation to Alternative Approaches

You might wonder if API automation is worth the effort versus manual dashboards or other tools. Here's the comparison:

Manual Dashboards

✗ 3-5 hours per week per person
✗ Data stale by Friday
✓ No setup cost
✗ Doesn't scale beyond 2-3 brands

Zapier/n8n Automation

✓ 1 hour setup, 0 hours ongoing
✓ Always current
✓ Scales to 50+ brands
✗ Less customization
~ $50-200/month depending on volume

Custom API Automation

✓ 4-8 hours setup, minimal ongoing
✓ Always current
✓ Scales to unlimited brands
✓ Full customization
~ $50-500/month for hosting (Lambda is cheap)

For agencies: Zapier/n8n wins if you have <10 clients. Custom API wins if you have >10.

For in-house teams: Custom API is worth it if you're doing AEO seriously. The time investment pays back in weeks.

Key Takeaways

Automating AEO reports via API eliminates manual work and gives your team real-time visibility into AI search performance. Here's what you've learned:

Store API keys securely in environment variables or secrets managers. Never expose them in code.
Fetch visibility and content data from the SearchFit API using the brand visibility and content performance endpoints.
Generate structured reports that combine multiple data sources and calculate trends, alerts, and recommendations.
Format for your destination: Email (HTML), Slack (Block Kit), Google Sheets (gspread), or BI tools (JSON).
Schedule with Lambda, n8n, or Zapier depending on your comfort with code and complexity.
Monitor and iterate: Add error handling, validate responses, and refine alert thresholds based on real data.
Scale for agencies: Use batch processing and client configuration files to manage dozens of brands.

The SearchFit API is your foundation. You can build simple weekly email reports or complex multi-client dashboards. Start small, get one report working, schedule it, then expand.

For more context on API-driven AEO reporting, check out this guide on AEO API implementation for custom reports, which covers integrations with Looker Studio, Power BI, and Tableau.

Your team's time is expensive. Automation is the fastest way to turn SearchFit data into actionable insights at scale.