I Built a Simple AI Visibility Tracker in Python. Here’s What Breaks When You Scale It
AI visibility tracking sounds like a fairly simple programming problem.
Ask ChatGPT a question.
Check whether a brand appears in the answer.
Save the result.
Repeat tomorrow.
And honestly, at first, it is that simple.
You can build a primitive AI visibility tracker in a few lines of Python.
from openai import OpenAI
from datetime import datetime
client = OpenAI()
brand = "Acme"
prompts = [
"What are the best project management tools?",
"What are good alternatives to Trello?",
"What project management software is best for small businesses?",
"Which project management tools have AI features?",
"What tools can remote teams use to organize projects?"
]
results = []
for prompt in prompts:
response = client.responses.create(
model="gpt-5.4-mini",
input=prompt
)
answer = response.output_text
results.append({
"prompt": prompt,
"mentioned": brand.lower() in answer.lower(),
"response": answer,
"checked_at": datetime.utcnow().isoformat()
})
visibility = (
sum(r["mentioned"] for r in results)
/ len(results)
) * 100
print(f"{brand} visibility: {visibility:.1f}%")
If Acme appears in two of five responses, we could call that 40% visibility.
Done.
Well... not quite.
I recently worked through this problem while building the AI visibility tracking system behind CrawlSpider, and the interesting part wasn't making the LLM API call.
It was everything that happened after that.
Conceptually, an AI visibility tracker looks something like this:
for each brand:
for each prompt:
for each model:
ask the model
save the response
find the brand
find competitors
calculate metrics
That looks harmless.
But consider:
100 brands
× 50 prompts
× 3 models
× daily scans
That's 15,000 requests every day.
Or 450,000 model responses every month.
Move to 1,000 brands and you're dealing with millions.
And suddenly this:
for prompt in prompts:
call_llm(prompt)
isn't really the architecture anymore.
The first thing that breaks is the simple loop.
What happens if request #8,742 fails?
What happens when an API starts returning rate-limit errors?
What if one provider slows down?
What if your worker crashes halfway through a batch?
You don't want to restart everything.
So the architecture starts becoming something like:
Scheduler
↓
Scan Generator
↓
Job Queue
↓
Worker Pool
↓
LLM Provider
↓
Response Store
Now you need:
We've moved surprisingly far away from our original Python script.
brand in response breaks
Our prototype has another wonderfully naive line:
brand.lower() in answer.lower()
Try that with:
brand = "Apple"
Did the model mention Apple Inc.?
Or an apple?
What about abbreviations?
Product names?
Parent companies?
And simply knowing that a brand appeared isn't particularly interesting.
Suppose the response says:
For enterprise teams I'd consider Acme or Monday.com,
while smaller teams might prefer Trello.
Now I probably want something closer to:
{
"target_brand": {
"mentioned": true,
"position": 1
},
"competitors": [
{"name": "Monday.com", "position": 2},
{"name": "Trello", "position": 3}
]
}
The tracker has quietly turned into an entity extraction and classification system too.
Here's another realization I had while working on this.
A single AI response isn't particularly valuable.
Change is valuable.
Imagine seeing this:
"Best project management software for small businesses?"
Week 1 Acme not mentioned
Week 2 Acme #5
Week 3 Acme #3
Week 4 Acme #2
That's interesting.
But now every observation potentially needs:
brand_id
prompt_id
model_id
model_version
timestamp
raw_response
brand_mentioned
brand_position
competitors
sentiment
citations
token_usage
latency
status
Multiply that by millions of responses.
You're not storing API results anymore.
You're building a historical analytics dataset.
Then you decide that monitoring one AI model isn't enough.
Maybe you want:
providers = [
"openai",
"anthropic",
"google"
]
Each has different APIs, response structures, rate limits, model identifiers, errors, citations and pricing.
Eventually you want an abstraction like:
┌── OpenAI Adapter
Prompt Engine ───┼── Anthropic Adapter
└── Google Adapter
↓
Normalized Response
Otherwise provider-specific logic ends up everywhere.
Then users ask for:
Prompt A → Daily
Prompt B → Weekly
Prompt C → Daily
Prompt D → Manual
Now something has to determine what is due.
And prevent duplicate runs.
And recover failed jobs.
And calculate the next run.
And make sure one huge account doesn't consume the entire worker pool.
At this point the "AI visibility tracker" is really a distributed job-processing and analytics application that happens to call LLMs.
There's another subtle problem.
Run:
What are the best tools for X?
today and your brand might appear.
Run the exact same prompt tomorrow and it might not.
That doesn't necessarily mean the brand suddenly became less visible.
LLM responses vary.
So when a dashboard says:
Visibility
Last week: 42%
This week: 38%
what does that actually mean?
Is something changing?
Or are we observing normal model variation?
This makes prompt consistency, sample size, model versions and historical comparison surprisingly important.
It's natural to focus on token costs.
Those certainly matter when you're running hundreds of thousands or millions of requests.
But I found the more interesting cost to be engineering complexity.
At scale you're paying for much more than inference:
LLM inference
+ queues
+ workers
+ databases
+ storage
+ scheduling
+ retries
+ observability
+ analytics
+ provider maintenance
+ engineering time
And every new dimension multiplies the workload:
brands
× prompts
× models
× scan frequency
× time
That's the equation I'd pay attention to when designing one of these systems.
One reason we were able to build this into CrawlSpider is that we weren't starting completely from zero.
I'd previously built pieces of this kind of infrastructure for other projects.
InfoCaptor had given us experience with analytics, visualization and AI-driven workflows.
CrawlSpider's existing internal-linking system already dealt with crawling, page analysis, background processing and large collections of URLs.
Other projects had already forced us to solve problems around scheduled jobs, APIs, queues and asynchronous processing.
The AI visibility tracker became less about inventing every component and more about assembling those existing patterns around a new workflow:
Brand
↓
Prompts
↓
Models
↓
Scheduled scans
↓
Responses
↓
Mentions + competitors
↓
Historical metrics
↓
Dashboard
That reuse turned out to be extremely valuable.
Could you build an AI visibility tracker yourself?
Absolutely.
In fact, I think building the five-prompt Python version is a great weekend project.
The core algorithm can fit on one screen.
But that's also what makes this problem interesting.
There is a huge gap between:
"Call an LLM and see if my brand appears."
and:
"Reliably monitor thousands of brands across
multiple models every day and explain how their
visibility is changing."
The first is an API call.
The second is a platform.
I wrote a much deeper breakdown of the architecture, scaling math, infrastructure and costs while documenting how we approached this at CrawlSpider:
How to Build an AI Visibility Tracker From Scratch
If you're building something similar, I'd be interested in hearing how you're approaching the scheduling, normalization and non-determinism problems.
PS:
I also built a AI Adoption visualization Dashboard , check out!
I also maintain LLM cutoff dates for major providers
https://www.crawlspider.com/llm-knowledge-cutoff-dates/
Lastly there are 50+ brands monitored for their AI Visibility