I Built a Simple AI Visibility Tracker in Python. Here’s What Breaks When You Scale It

A developer at CrawlSpider described building an AI visibility tracker in Python that queries LLM APIs to check whether brands appear in model answers, and detailed how the naive approach breaks at scale. The simple loop of prompting a model and string-matching the brand name fails under load — 100 brands across 50 prompts and 3 models means 15,000 daily requests — requiring a scheduler, job queue, worker pool, and response store, while basic substring matching also fails for ambiguous names like Apple and must be replaced with entity extraction and competitor classification. The developer argued that tracking change over time, not a single response, is where the real value lies.

I Built a Simple AI Visibility Tracker https://www.crawlspider.com/ 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. 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 https://www.crawlspider.com/how-to-build-an-ai-visibility-tracker-from-scratch/ , 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 https://www.crawlspider.com/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 https://www.crawlspider.com/pages/ai-adoption-rate/ , check out I also maintain LLM cutoff dates for major providers https://www.crawlspider.com/llm-knowledge-cutoff-dates/ https://www.crawlspider.com/llm-knowledge-cutoff-dates/ Lastly there are 50+ brands monitored for their AI Visibility https://www.crawlspider.com/geo/