Developer details how a simple AI brand tracker breaks down at scale

A developer building an AI visibility tracker for CrawlSpider shared how a basic Python script that checks whether a brand appears in LLM responses quickly becomes inadequate at scale. While a few lines of code suffice for tracking one brand across a handful of prompts, scaling to 100 brands, 50 prompts, and 3 models generates 15,000 API requests daily. At that volume, the architecture must evolve to include job queues, worker pools, retry logic, rate-limit management, and idempotency controls. Simple string matching also proves unreliable, as brand names like 'Apple' can be ambiguous, prompting the need for entity extraction and competitor classification. The developer further noted that tracking rank changes over time — not just binary mentions — is what makes the data genuinely useful.