How to Build an AI Visibility Tracker From Scratch

Summary

The article explains why monitoring whether AI models mention a company becomes a substantial data and analytics platform at production scale. A small Python script can submit prompts, save responses, detect a brand mention, and calculate a basic visibility percentage. But 100 brands monitored across 50 prompts and three models each day would generate 15,000 requests daily, while 1,000 brands would produce 4.5 million responses per month under the same assumptions. The article estimates that illustrative model-token costs can remain relatively modest, although search, response length, model choice, and additional providers change the total; OpenAI’s Batch API is cited as a possible 50% discount for asynchronous workloads. The larger engineering problems include queues, worker pools, rate limits, retries, idempotency, failure recovery, scheduling, and provider-specific adapters. Production analysis also has to distinguish brands from ambiguous terms, identify products and competitors, rank mentions, track sentiment and citations, and preserve raw responses and derived metrics over time. Because model responses are nondeterministic, changes in visibility may reflect sampling variation rather than a real brand change. The article concludes that the expensive part is often the engineering needed to operate millions of observations reliably, not the basic API call.