Guide17 Apr 2026

Building Custom AEO Workflows With the SearchFit API

Learn to build custom Answer Engine Optimization workflows using SearchFit's API. Step-by-step guide for engineers to integrate AI visibility tracking.

KKevin16 minutes read

Building custom aeo workflows with the SearchFit API

Understanding Custom AEO Workflows and the SearchFit API

Your brand is getting cited in ChatGPT, Perplexity, and Claude, but you have no way to know it. Meanwhile, your competitors are tracking every mention, optimizing content in real time, and capturing the traffic that used to flow through organic search. This is the reality of Answer Engine Optimization (AEO): visibility without measurement is worthless.

The SearchFit API exists to close that gap. It's not a dashboard-only tool; it's a programmatic foundation that lets you build custom workflows tailored to your team's exact needs. Whether you're a SaaS company monitoring AI citations across dozens of products, an e-commerce brand tracking how generative engines recommend your competitors' content, or an agency managing AEO visibility for multiple clients, the API lets you move beyond point-and-click monitoring into systematic, scalable optimization.

This guide walks you through building production-grade AEO workflows using the SearchFit API. You'll learn how to compose API calls, integrate them into your existing tools, automate content analysis, and feed insights back into your optimization cycle. We assume you have basic HTTP knowledge and familiarity with REST APIs; we'll show you how to wire everything together.

Prerequisites and Setup

Before you start building, confirm you have the following in place:

API Access and Authentication

You'll need a valid SearchFit API key. If you don't have one, log into your SearchFit account and generate a key from the developer settings. Store it securely, treat it like a database password. All API calls require the key in the Authorization header as a Bearer token.

Development Environment

You'll need a code editor, a terminal, and a tool for testing HTTP requests. curl is sufficient; Postman or Insomnia give you a GUI if you prefer. If you're building a production workflow, you'll want Node.js, Python, or your language of choice installed.

Workflow Integration Platform (Optional but Recommended)

If you're not writing custom code, you can use no-code platforms to compose API calls. Zapier Webhooks Documentation explains how to trigger workflows via API. Make.com Webhook Module Reference covers similar webhook-based automation. n8n's Custom API node lets you build HTTP-based workflows locally. Custom Workflows and the Workflows API provides a framework for thinking about API-driven automation pipelines.

Understanding Your Data Needs

Before writing a single API call, map out what you need to track. Are you monitoring brand mentions across all AI engines? Tracking specific product keywords? Measuring citation sources (which engine cited you, in what context)? Building content recommendations based on ranking gaps? Your answer determines which endpoints you'll hit and how often.

Step 1: Authenticate and Test the API Connection

Start with a simple connectivity test. This confirms your API key works and your environment is configured correctly.

Using curl:

curl -X GET https://api.searchfit.ai/v1/account \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json"

Replace YOUR_API_KEY with your actual key. If successful, you'll receive a JSON response with your account details, including your organization ID and available API quota.

Expected Response:

{
"organization_id": "org_abc123",
"account_name": "Your Company",
"api_quota": {
"requests_per_month": 100000,
"requests_used": 1234
},
"engines_tracked": ["chatgpt", "perplexity", "gemini", "claude", "grok", "google"]
}

If you get a 401 error, your API key is invalid or expired. Double-check the key and regenerate it if needed. If you get a 403 error, your account doesn't have API access; contact SearchFit support to enable it.

Pro Tip: Store your API key in an environment variable, not in code. In your terminal:

export SEARCHFIT_API_KEY="your_key_here"

Then reference it in requests:

curl -X GET https://api.searchfit.ai/v1/account \
-H "Authorization: Bearer $SEARCHFIT_API_KEY" \
-H "Content-Type: application/json"

Step 2: Retrieve Your Tracked Keywords and Current Rankings

Now that you're authenticated, fetch the keywords you're monitoring and their current visibility across AI engines. This is the foundation of any AEO workflow, you can't optimize what you don't measure.

API Endpoint: List Tracked Keywords

curl -X GET https://api.searchfit.ai/v1/keywords \
-H "Authorization: Bearer $SEARCHFIT_API_KEY" \
-H "Content-Type: application/json"

Response Structure:

{
"keywords": [
{
"keyword_id": "kw_xyz789",
"keyword": "project management software",
"category": "product",
"created_at": "2024-01-15T10:30:00Z",
"tracking_engines": ["chatgpt", "perplexity", "gemini"],
"visibility_score": 68.5
},
{
"keyword_id": "kw_abc456",
"keyword": "agile collaboration tools",
"category": "product",
"created_at": "2024-01-20T14:22:00Z",
"tracking_engines": ["chatgpt", "claude", "grok"],
"visibility_score": 42.1
}
],
"total_keywords": 47,
"pagination": {
"page": 1,
"limit": 50,
"total_pages": 1
}
}

Each keyword returns a visibility score (0-100) indicating how prominently your brand appears in AI results for that term. Scores below 50 signal optimization opportunities; scores above 75 mean you're performing well but should maintain your position.

Filtering Keywords by Category or Engine:

If you're tracking dozens of keywords, filter by category or engine to focus your workflow:

curl -X GET "https://api.searchfit.ai/v1/keywords?category=product&engine=perplexity" \
-H "Authorization: Bearer $SEARCHFIT_API_KEY" \
-H "Content-Type: application/json"

Practical Use Case: In a production workflow, you'd fetch keywords with visibility scores below a threshold (e.g., 50) and queue them for content optimization. This automated triage saves your team hours of manual review.

Step 3: Fetch Detailed Rankings and Citation Data

Visibility scores are directional, but you need granular data to understand why your visibility is low and what content is actually ranking.

API Endpoint: Get Rankings for a Specific Keyword

curl -X GET https://api.searchfit.ai/v1/keywords/kw_xyz789/rankings \
-H "Authorization: Bearer $SEARCHFIT_API_KEY" \
-H "Content-Type: application/json"

Response Structure:

{
"keyword_id": "kw_xyz789",
"keyword": "project management software",
"rankings": {
"chatgpt": {
"position": 3,
"cited": true,
"citation_count": 7,
"last_updated": "2024-02-10T08:15:00Z",
"sources": [
{
"url": "https://yoursite.com/project-management-guide",
"title": "The Complete Guide to Project Management Tools",
"citation_type": "direct_mention",
"context": "Our research shows that project management software has evolved significantly. Tools like Asana, Monday.com, and your-brand offer robust collaboration features."
},
{
"url": "https://yoursite.com/case-study-enterprise",
"title": "Enterprise Implementation Case Study",
"citation_type": "referenced_content",
"context": "Enterprise teams report a 40% improvement in project delivery timelines when using modern software solutions."
}
]
},
"perplexity": {
"position": 5,
"cited": true,
"citation_count": 4,
"last_updated": "2024-02-10T08:15:00Z",
"sources": [
{
"url": "https://yoursite.com/comparison-monday-vs-asana",
"title": "Monday.com vs Asana: Detailed Comparison",
"citation_type": "direct_mention",
"context": "When comparing project management tools, Monday.com and Asana are frequently recommended alongside your-brand for mid-market teams."
}
]
},
"gemini": {
"position": null,
"cited": false,
"citation_count": 0,
"last_updated": "2024-02-10T08:15:00Z",
"sources": []
}
}
}

This response tells you:

Position: Your rank in AI results (1 is best, null means not cited)
Citation count: How many times your content appears across AI responses
Sources: Which specific pages are being cited and in what context
Citation type: Whether you're mentioned directly or your content is paraphrased

Critical Insight: Notice you rank #3 in ChatGPT but aren't cited in Gemini at all. Your workflow should flag this gap and trigger content optimization for Gemini-specific keywords.

Step 4: Analyze Citation Gaps and Competitive Positioning

Now that you have your own rankings, fetch competitor data to understand the competitive landscape. This reveals which competitors are outranking you and why.

API Endpoint: Competitor Rankings

curl -X GET https://api.searchfit.ai/v1/keywords/kw_xyz789/competitors \
-H "Authorization: Bearer $SEARCHFIT_API_KEY" \
-H "Content-Type: application/json"

Response Structure:

{
"keyword_id": "kw_xyz789",
"keyword": "project management software",
"competitors": [
{
"domain": "asana.com",
"brand_name": "Asana",
"chatgpt_position": 1,
"perplexity_position": 1,
"gemini_position": 2,
"average_visibility": 87.3,
"citation_count": 23
},
{
"domain": "monday.com",
"brand_name": "Monday.com",
"chatgpt_position": 2,
"perplexity_position": 2,
"gemini_position": 1,
"average_visibility": 84.1,
"citation_count": 21
},
{
"domain": "yoursite.com",
"brand_name": "Your Brand",
"chatgpt_position": 3,
"perplexity_position": 5,
"gemini_position": null,
"average_visibility": 55.7,
"citation_count": 11
}
]
}

Building a Competitive Gap Analysis:

In your workflow, calculate the difference between your visibility and the top competitor:

top_competitor_visibility = 87.3
your_visibility = 55.7
gap = top_competitor_visibility - your_visibility
print(f"Visibility gap: {gap}% (you're {gap}% behind the leader)")
# Output: Visibility gap: 31.6% (you're 31.6% behind the leader)

A 31.6% gap is significant. Your workflow should:

Identify which of Asana's pages rank highest
Analyze what makes them rank well (content depth, keyword coverage, freshness)
Flag your content gaps and recommend optimization priorities

Step 5: Build an Automated Content Optimization Workflow

This is where the API becomes truly powerful. You're going to wire together ranking data, competitive analysis, and your content management system to automatically identify and prioritize optimization opportunities.

Workflow Architecture:

[SearchFit API] → [Parse Rankings] → [Identify Gaps] → [Generate Recommendations] → [Trigger Content Updates]

Step 5A: Fetch Low-Performing Keywords Programmatically

Create a script that runs daily and pulls keywords with visibility below your threshold:

import requests
import json
from datetime import datetime

API_KEY = "your_api_key"
BASE_URL = "https://api.searchfit.ai/v1"
VISIBILITY_THRESHOLD = 50  # Flag keywords below 50%

def get_low_performing_keywords():
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}

response = requests.get(
f"{BASE_URL}/keywords",
headers=headers
)

keywords = response.json()["keywords"]
low_performers = [
kw for kw in keywords 
if kw["visibility_score"] < VISIBILITY_THRESHOLD
]

return low_performers

low_performers = get_low_performing_keywords()
print(f"Found {len(low_performers)} keywords below {VISIBILITY_THRESHOLD}% visibility")

for kw in low_performers:
print(f"  - {kw['keyword']}: {kw['visibility_score']}%")

Output Example:

Found 12 keywords below 50% visibility
- agile collaboration tools: 42.1%
- team productivity software: 38.7%
- resource allocation platform: 45.3%

Step 5B: Fetch Detailed Rankings and Identify Content Gaps

For each low-performing keyword, get detailed rankings to see which engines aren't citing you:

def analyze_ranking_gaps(keyword_id):
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}

response = requests.get(
f"{BASE_URL}/keywords/{keyword_id}/rankings",
headers=headers
)

rankings = response.json()["rankings"]
gaps = []

for engine, data in rankings.items():
if not data["cited"]:
gaps.append({
"engine": engine,
"reason": "Not cited",
"priority": "high"
})
elif data["position"] and data["position"] > 3:
gaps.append({
"engine": engine,
"position": data["position"],
"reason": "Below top 3",
"priority": "medium"
})

return gaps

# Example: Analyze gaps for "agile collaboration tools"
gaps = analyze_ranking_gaps("kw_abc456")
for gap in gaps:
print(f"  {gap['engine']}: {gap['reason']} (Priority: {gap['priority']})")

Output Example:

gemini: Not cited (Priority: high)
claude: Below top 3 (Priority: medium)

Step 5C: Generate Content Recommendations

Now fetch competitor data to understand what's working for them:

def generate_optimization_recommendations(keyword_id):
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}

# Get your rankings
your_rankings = requests.get(
f"{BASE_URL}/keywords/{keyword_id}/rankings",
headers=headers
).json()["rankings"]

# Get competitor rankings
competitors = requests.get(
f"{BASE_URL}/keywords/{keyword_id}/competitors",
headers=headers
).json()["competitors"]

recommendations = []

# Identify engines where competitors rank but you don't
for competitor in competitors[:3]:  # Top 3 competitors
for engine, comp_data in competitor.items():
if engine in ["chatgpt_position", "perplexity_position", "gemini_position"]:
engine_name = engine.replace("_position", "")

if your_rankings[engine_name]["cited"] == False and comp_data is not None:
recommendations.append({
"action": f"Optimize for {engine_name}",
"reason": f"{competitor['brand_name']} ranks #{comp_data} on {engine_name}, you're not cited",
"priority": "high"
})

return recommendations

recs = generate_optimization_recommendations("kw_abc456")
for rec in recs:
print(f"  [{rec['priority'].upper()}] {rec['action']}: {rec['reason']}")

Output Example:

[HIGH] Optimize for gemini: Asana ranks #2 on gemini, you're not cited
[HIGH] Optimize for claude: Monday.com ranks #1 on claude, you're not cited

Step 6: Integrate with Your Content Management System

Recommendations are only useful if they trigger action. Wire the SearchFit API into your CMS or content workflow platform to automatically create optimization tasks.

Using the SearchFIT WordPress Plugin:

If you're using WordPress, the SearchFIT WordPress Plugin provides a secure webhook API for creating posts in AI-powered content workflows. Install it, configure your webhook endpoint, and your optimization workflow can automatically:

Create draft posts for low-visibility keywords
Populate them with competitor analysis and optimization guidelines
Route them to your content team for review and publication

Webhook Integration Example:

def create_optimization_task(keyword, recommendations):
webhook_url = "https://yoursite.com/wp-json/searchfit/v1/optimization-task"

payload = {
"keyword": keyword,
"visibility_score": keyword["visibility_score"],
"recommendations": recommendations,
"priority": "high" if keyword["visibility_score"] < 40 else "medium",
"created_at": datetime.now().isoformat()
}

response = requests.post(
webhook_url,
json=payload,
headers={"Content-Type": "application/json"}
)

return response.status_code == 201

# Trigger task creation
for kw in low_performers:
recs = generate_optimization_recommendations(kw["keyword_id"])
if recs:
create_optimization_task(kw, recs)
print(f"Created optimization task for: {kw['keyword']}")

No-Code Workflow Platforms:

If you prefer not to write code, you can build the same workflow using no-code platforms:

Zapier: Use Zapier Webhooks Documentation to trigger workflows when SearchFit detects low visibility. Zapier can then create tasks in Asana, Monday.com, or your project management tool.
Make (Integromat): Make.com Webhook Module Reference lets you set up webhook listeners that trigger custom workflows. You can fetch SearchFit data, analyze it, and create content tasks automatically.
n8n: Custom API Node Documentation enables you to build HTTP-based workflows locally. You can compose SearchFit API calls, parse the response, and trigger downstream actions.

All three platforms support conditional logic, so you can route high-priority gaps to your top content creators and lower-priority items to junior writers.

Step 7: Monitor Content Performance and Close the Loop

Once you've published optimized content, track how it impacts your AEO rankings. This closes the feedback loop and proves ROI.

API Endpoint: Fetch Historical Rankings

curl -X GET "https://api.searchfit.ai/v1/keywords/kw_xyz789/history?days=30" \
-H "Authorization: Bearer $SEARCHFIT_API_KEY" \
-H "Content-Type: application/json"

Response Structure:

{
"keyword_id": "kw_xyz789",
"keyword": "project management software",
"history": [
{
"date": "2024-01-15",
"chatgpt_position": 5,
"perplexity_position": 8,
"gemini_position": null,
"visibility_score": 42.1
},
{
"date": "2024-01-22",
"chatgpt_position": 4,
"perplexity_position": 7,
"gemini_position": null,
"visibility_score": 48.5
},
{
"date": "2024-02-10",
"chatgpt_position": 3,
"perplexity_position": 5,
"gemini_position": 6,
"visibility_score": 68.5
}
]
}

Building a Performance Dashboard:

Create a simple dashboard that tracks ranking improvements over time:

def calculate_ranking_improvement(keyword_id, days=30):
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}

response = requests.get(
f"{BASE_URL}/keywords/{keyword_id}/history?days={days}",
headers=headers
)

history = response.json()["history"]

if len(history) < 2:
return None

start_visibility = history[0]["visibility_score"]
end_visibility = history[-1]["visibility_score"]
improvement = end_visibility - start_visibility

return {
"keyword_id": keyword_id,
"start_visibility": start_visibility,
"end_visibility": end_visibility,
"improvement": improvement,
"improvement_percent": (improvement / start_visibility) * 100
}

# Track improvements for all keywords
for kw in low_performers:
improvement = calculate_ranking_improvement(kw["keyword_id"])
if improvement:
print(f"{kw['keyword']}: {improvement['improvement']:.1f}% improvement")

Output Example:

project management software: +26.4% improvement
agile collaboration tools: +8.2% improvement
team productivity software: +12.1% improvement

These metrics prove that your AEO efforts are working. Share them with stakeholders to justify continued investment in AI visibility optimization.

Step 8: Scale Your Workflow Across Multiple Clients or Products

If you're an agency managing AEO for multiple clients, or a SaaS company with multiple product lines, you need to scale your workflow without multiplying your workload.

Using Organization and Account Segmentation:

The SearchFit API supports organization-level and account-level filtering. Fetch rankings across all your tracked brands in a single API call:

def get_all_tracked_brands():
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}

response = requests.get(
f"{BASE_URL}/organization/brands",
headers=headers
)

return response.json()["brands"]

brands = get_all_tracked_brands()
print(f"Tracking {len(brands)} brands")

for brand in brands:
print(f"  - {brand['name']}: {brand['visibility_score']}% average visibility")

Batching API Calls:

If you have 50+ keywords, don't make individual API calls for each one. Use batch endpoints to fetch multiple keywords' data in a single request:

def get_batch_rankings(keyword_ids):
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}

payload = {
"keyword_ids": keyword_ids
}

response = requests.post(
f"{BASE_URL}/keywords/batch/rankings",
json=payload,
headers=headers
)

return response.json()["rankings"]

# Fetch rankings for 50 keywords in one call
keyword_ids = [kw["keyword_id"] for kw in all_keywords]
batch_rankings = get_batch_rankings(keyword_ids)
print(f"Fetched rankings for {len(batch_rankings)} keywords")

Batching reduces API calls by 50x, speeds up your workflow, and stays well within rate limits even at scale.

Scheduling Regular Runs:

Use a job scheduler (cron, GitHub Actions, AWS Lambda) to run your workflow automatically:

# Run AEO analysis daily at 8 AM
0 8 * * * /usr/bin/python3 /home/user/aeo_workflow.py

Or deploy as a serverless function:

# AWS Lambda handler
def lambda_handler(event, context):
low_performers = get_low_performing_keywords()

for kw in low_performers:
gaps = analyze_ranking_gaps(kw["keyword_id"])
recs = generate_optimization_recommendations(kw["keyword_id"])

if recs:
create_optimization_task(kw, recs)

return {
"statusCode": 200,
"body": f"Processed {len(low_performers)} keywords"
}

Step 9: Advanced Workflow Patterns

Once you have the basics running, layer in more sophisticated patterns.

Real-Time Alerts for Ranking Changes

Set up webhooks to notify you when a keyword's ranking drops significantly:

def register_ranking_change_webhook(keyword_id, threshold_percent=10):
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}

payload = {
"keyword_id": keyword_id,
"event_type": "ranking_change",
"threshold_percent": threshold_percent,
"webhook_url": "https://yoursite.com/webhooks/ranking-alert"
}

response = requests.post(
f"{BASE_URL}/webhooks/register",
json=payload,
headers=headers
)

return response.json()["webhook_id"]

# Alert if "project management software" drops >10% in visibility
webhook_id = register_ranking_change_webhook("kw_xyz789", threshold_percent=10)
print(f"Webhook registered: {webhook_id}")

When a ranking change exceeds your threshold, SearchFit sends a POST request to your webhook URL with the change details. Your webhook handler can then notify Slack, create a Jira ticket, or trigger an emergency content review.

Predictive Content Recommendations

Combine SearchFit ranking data with your content management system to predict which topics will drive the most AEO traffic:

def score_content_topics(keywords, competitor_data):
"""Score topics by AEO opportunity: visibility gap × search volume × citation trend"""

scored_topics = []

for kw in keywords:
# Visibility gap: how far behind the leader are you?
top_competitor_visibility = competitor_data[kw["keyword_id"]][0]["visibility_score"]
your_visibility = kw["visibility_score"]
visibility_gap = top_competitor_visibility - your_visibility

# Trend: is visibility improving or declining?
trend_score = get_visibility_trend(kw["keyword_id"])

# Opportunity score: gap × trend (higher = more urgent)
opportunity_score = visibility_gap * trend_score

scored_topics.append({
"keyword": kw["keyword"],
"opportunity_score": opportunity_score,
"gap": visibility_gap,
"trend": trend_score
})

# Sort by opportunity score (descending)
return sorted(scored_topics, key=lambda x: x["opportunity_score"], reverse=True)

# Get top 10 opportunities
top_opportunities = score_content_topics(all_keywords, competitor_rankings)[:10]
for topic in top_opportunities:
print(f"{topic['keyword']}: Opportunity Score {topic['opportunity_score']:.1f}")

Multi-Engine Content Strategies

Different AI engines have different citation patterns. ChatGPT favors comprehensive guides; Perplexity cites recent news and research; Claude prefers nuanced analysis. Tailor your content strategy by engine:

def get_engine_specific_strategy(keyword_id):
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}

rankings = requests.get(
f"{BASE_URL}/keywords/{keyword_id}/rankings",
headers=headers
).json()["rankings"]

strategy = {}

# ChatGPT: focus on comprehensive, evergreen content
if rankings["chatgpt"]["cited"]:
strategy["chatgpt"] = "Maintain comprehensive guide; update annually"
else:
strategy["chatgpt"] = "Create 3000+ word definitive guide"

# Perplexity: focus on fresh, recent content
if rankings["perplexity"]["cited"]:
strategy["perplexity"] = "Update with recent case studies and trends"
else:
strategy["perplexity"] = "Publish recent research or industry analysis"

# Claude: focus on nuanced, analytical content
if rankings["claude"]["cited"]:
strategy["claude"] = "Maintain deep analysis and expert perspective"
else:
strategy["claude"] = "Create comparison or pros/cons analysis"

return strategy

strategy = get_engine_specific_strategy("kw_xyz789")
for engine, tactic in strategy.items():
print(f"{engine}: {tactic}")

Understanding API Rate Limits and Best Practices

As you scale, respect API rate limits and follow best practices to avoid throttling.

Rate Limits

SearchFit API allows 10,000 requests per day for standard accounts, 50,000 for enterprise. Each request costs 1 point regardless of endpoint. Batch requests count as 1 point, so use them liberally.

Caching

Don't fetch the same keyword rankings every hour. Cache results locally and refresh on a schedule:

import json
from datetime import datetime, timedelta

CACHE_FILE = "rankings_cache.json"
CACHE_TTL_HOURS = 6  # Refresh cache every 6 hours

def get_cached_rankings(keyword_id):
if not os.path.exists(CACHE_FILE):
return None

with open(CACHE_FILE, "r") as f:
cache = json.load(f)

if keyword_id not in cache:
return None

cached_data = cache[keyword_id]
cached_time = datetime.fromisoformat(cached_data["timestamp"])

if datetime.now() - cached_time < timedelta(hours=CACHE_TTL_HOURS):
return cached_data["rankings"]

return None

def cache_rankings(keyword_id, rankings):
cache = {}

if os.path.exists(CACHE_FILE):
with open(CACHE_FILE, "r") as f:
cache = json.load(f)

cache[keyword_id] = {
"rankings": rankings,
"timestamp": datetime.now().isoformat()
}

with open(CACHE_FILE, "w") as f:
json.dump(cache, f)

# Use cached data if available
cached = get_cached_rankings("kw_xyz789")
if cached:
rankings = cached
else:
rankings = fetch_rankings("kw_xyz789")
cache_rankings("kw_xyz789", rankings)

Error Handling

Network requests fail. Handle retries gracefully:

import time

def fetch_with_retry(url, max_retries=3, backoff_factor=2):
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}

for attempt in range(max_retries):
try:
response = requests.get(url, headers=headers, timeout=10)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
if attempt == max_retries - 1:
raise

wait_time = backoff_factor ** attempt
print(f"Request failed. Retrying in {wait_time}s...")
time.sleep(wait_time)

Connecting Workflows to Your Analytics Platform

Your SearchFit API workflow should feed into your Analytics | SearchFIT dashboard for visibility. You can also push data to external analytics platforms.

Exporting to Google Sheets

Create a live spreadsheet of your AEO performance:

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

# Authenticate with Google Sheets
scope = ["https://www.googleapis.com/auth/spreadsheets"]
creds = service_account.Credentials.from_service_account_file(
"google_creds.json", scopes=scope
)
client = gspread.authorize(creds)

# Open spreadsheet
sheet = client.open("AEO Performance Dashboard").sheet1

# Write header
sheet.append_row(["Keyword", "Visibility", "ChatGPT Position", "Perplexity Position", "Gemini Position"])

# Write data
for kw in all_keywords:
rankings = fetch_rankings(kw["keyword_id"])
sheet.append_row([
kw["keyword"],
kw["visibility_score"],
rankings["chatgpt"]["position"],
rankings["perplexity"]["position"],
rankings["gemini"]["position"]
])

print("Data exported to Google Sheets")

Pushing to Your Data Warehouse

For enterprise workflows, push SearchFit data into your data warehouse:

import snowflake.connector

conn = snowflake.connector.connect(
user="your_user",
password="your_password",
account="your_account",
warehouse="your_warehouse",
database="your_database"
)

cursor = conn.cursor()

# Create table if it doesn't exist
cursor.execute("""
CREATE TABLE IF NOT EXISTS aeo_rankings (
keyword_id VARCHAR,
keyword VARCHAR,
visibility_score FLOAT,
chatgpt_position INT,
perplexity_position INT,
gemini_position INT,
timestamp TIMESTAMP
)
""")

# Insert data
for kw in all_keywords:
rankings = fetch_rankings(kw["keyword_id"])
cursor.execute(
"INSERT INTO aeo_rankings VALUES (%s, %s, %s, %s, %s, %s, %s)",
(
kw["keyword_id"],
kw["keyword"],
kw["visibility_score"],
rankings["chatgpt"]["position"],
rankings["perplexity"]["position"],
rankings["gemini"]["position"],
datetime.now()
)
)

conn.commit()
print("Data loaded to Snowflake")

Monitoring Your Workflow's Health

As your workflow grows, monitor its health. Track API usage, error rates, and execution time.

Logging and Alerting

import logging

logging.basicConfig(
filename="aeo_workflow.log",
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s"
)

def run_aeo_workflow():
try:
logging.info("Starting AEO workflow")

low_performers = get_low_performing_keywords()
logging.info(f"Found {len(low_performers)} low-performing keywords")

processed = 0
for kw in low_performers:
gaps = analyze_ranking_gaps(kw["keyword_id"])
recs = generate_optimization_recommendations(kw["keyword_id"])

if recs:
create_optimization_task(kw, recs)
processed += 1

logging.info(f"Created {processed} optimization tasks")
logging.info("AEO workflow completed successfully")

except Exception as e:
logging.error(f"Workflow failed: {str(e)}", exc_info=True)
# Send alert to Slack
send_slack_alert(f"AEO workflow failed: {str(e)}")

run_aeo_workflow()

Summary and Key Takeaways

You now have a complete framework for building custom AEO workflows with the SearchFit API. Here's what you've learned:

Core Concepts

The SearchFit API gives you programmatic access to your AEO rankings across ChatGPT, Perplexity, Gemini, Claude, Grok, and Google
Visibility scores and ranking positions are the foundation; competitive analysis reveals optimization opportunities
Automated workflows turn data into action, identifying gaps, generating recommendations, and triggering content updates

Practical Steps

Authenticate and test your API connection
Fetch your tracked keywords and current visibility scores
Analyze detailed rankings and citation sources
Compare your performance to competitors
Build automated content optimization workflows
Integrate with your CMS or content workflow platform
Monitor performance and close the feedback loop
Scale across multiple clients or product lines
Layer in advanced patterns (alerts, predictions, engine-specific strategies)

Best Practices

Use batch endpoints to reduce API calls
Cache results locally and refresh on a schedule
Implement retry logic and error handling
Monitor API usage and respect rate limits
Log all workflow activity for debugging and auditing
Push data to your analytics platform for visibility

Next Steps

Set up your API key and test connectivity
Build a script to fetch your low-performing keywords
Create an optimization recommendation engine
Wire it into your CMS or project management tool
Schedule it to run daily and monitor results
Share performance metrics with stakeholders

The SearchFit API is your lever for scaling AEO across your entire organization. Start small, fetch keywords, analyze gaps, generate recommendations. Once you have the core workflow running, layer in complexity: predictive scoring, engine-specific strategies, real-time alerts.

Your competitors are already optimizing for AI search. The API gives you the tools to outpace them systematically. Build, measure, iterate.

For more information about tracking your AI Tracking | SearchFIT performance and accessing detailed Analytics | SearchFIT, visit the SearchFit platform. And to get started with API integration, visit SearchFIT - #1 Automated AI Search Engine Optimization Platform for ChatGPT, Perplexity, Gemini, Grok, Claude and Google to set up your developer account.