... prove it moved anything? Screenshotting chatbot answers every morning isn't a metric. The honest truth is that GEO is still largely a black box. But "black box" doesn't mean "unmeasurable." "If you can't measure it, you can't improve it." Peter Drucker GEO starts with one thing: a scoring baseline you can run consistently. Same site, same input, same score every time. That's exactly what the service I'm sharing today does. It measures

... built a mastering tool that measures AI exports instead of guessing at them. Reply with a link to one of your Suno or Udio tracks and I'll run it and tell you what it actually measured

... were often the ones who left the harshest feedback. Meanwhile, users who opened the app twice a week for five minutes were writing us emails about how it changed how they handle difficult conversations. We were measuring activity when we should have been measuring

... every day? We tracked the top 10% of accounts by engagement. Their most-used feature was the Workflow Engine. Not because they requested it. Because it saved them time. They built automation for content publishing, citation alerts, and competitor tracking. That told us more than any sales call. 2. Support ticket clusters What problems keep coming up? We used RAISA to cluster 1,200 support tickets. The largest cluster (34%) was "automation rules are confusing." Not "add app store tracking ... ... correlation with retention. That told us where to invest. What we decided Building now: Workflow Engine v2 Conditional logic, scheduled runs, webhook triggers. 78% adoption, 64% retention. This is what keeps people around. RAIVE enhancements Real-time citation alerts, competitor

... same 1,000 questions to 5 AI models: ChatGPT (GPT-4), Gemini (Ultra), Perplexity (Pro), Claude (4.5 Sonnet), and Llama (3). We ran the experiment daily for 30 days. We tracked every citation at the source level. The goal: measure citation overlap. How often do these models cite the same source for the same question? The dataset: 1,000 questions 30 days 5 models = 150,000 total answer sets 47,382 unique citations tracked 12 industries with at least ... ... Manufacturing 22.3% Education 21.0% Real Estate 19.7% E commerce 18.4% Media 16.2% Consumer Goods 14.9% Travel 13.5% Professional Services 12.1% Finance and healthcare had the highest overlap. Professional services and travel had the lowest. Platform stability: We also measured

... Every API testing eval we found either required source code access, relied on rich documentation, or measured output format rather than whether a test would catch a real failure. So we built APIEval-20 . Twenty scenarios across e-commerce, payments, auth, scheduling, and user management. Each scenario gives a model exactly two things: a JSON schema and a sample payload . No implementation details, no docs, no further context. The model has to generate a test suite from that alone ... ... back. Scoring weights bug detection at 70%, API surface coverage at 20%, and test efficiency at 10%. We built this because we kept asking ourselves: how well can an AI agent actually think like a QA engineer? Most benchmarks measure

... tested them all building the same "Todo" app from scratch using identical prompts, requirements, and a stopwatch. The goal: Which AI tool actually delivers production-ready output, not just pretty demos? Watch the full head-to-heads: JustCopy.ai vs Base44: https://youtu.be/c79HJaWz6Y8 JustCopy.ai vs

... enough for you it is also good. BUT... You need to pay your bills, you need to pay for developers, you need to survive somehow, so the profit matters. :) Comment from Olga Aleksiutovich(@monolgaz): agree with Nika. The only measure of the success' app is making the money. It could be measured