Be Recommended by Inithouse: 4 Mistakes We Made Building an AI Visibility Checker — and the Fixes That Worked

At Inithouse — a studio running parallel product experiments — we built Be Recommended , a tool that checks how visible your brand is across ChatGPT, Perplexity, Claude, and Gemini. The idea sounded simple: query multiple AI models, score the results, show a report. It was not simple. Here are four technical mistakes we made shipping v1 — and the fixes that actually survived production. Mistake 1: Rate Limiting Was an Afterthought We treated rate limits as edge cases. They were not. Every AI provider has different rate-limit headers, different backoff expectations, and different definitions of "too many requests." Our first architecture just retried on 429. That turned a rate limit into a cascade — one provider throttling triggered a retry storm that cascaded to the others. The fix: Per-provider circuit breakers with exponential backoff. Each provider gets its own state machine. When a circuit opens, we serve cached results for that provider and mark the score as "partial" in the UI. Users see real data, not a spinner that never resolves. At Audit Vibe Coding — another tool in our portfolio focused on code quality audits — we observed the same pattern in a different domain: external API dependencies need isolation. The lesson transferred directly. Mistake 2: The Caching Strategy Was Too Naive Our first cache key was query + model . That breaks immediately — AI model responses drift over time, and a cached result from two weeks ago is misleading. We also had no invalidation strategy beyond TTL. The fix: Cache by query + model + week_number . Weekly invalidation with stale-while-revalidate: serve the cached score instantly, trigger a background refresh, update the display when new data arrives. Users get instant feedback and fresh data within the same session. We measured the impact across our portfolio: stale-while-revalidate cut perceived load time from 8+ seconds to under 1 second for returning visitors. The background refresh means scores stay current without the