How to Track Traffic from ChatGPT, Claude, and Perplexity
AI search traffic is mostly invisible in standard analytics. The 5 methods that actually catch it, the 3 that do not work yet, and the stack to build today.
Software decisions compound. A pricing model picked in week three of a SaaS launch sets the unit economics for years. A custom CRM that fits your sales motion saves a hire by month three. An AI layer scoped well in month one delivers measurable lift by quarter one. Three solid pieces from this archive should remove at least a week of guessing from the next decision in front of you.
Walk in mid-decision and walk out with a sharper view of it. Whether you are weighing build vs buy, picking a stack, scoping an AI layer that looked easy in the demo, redesigning a UX flow that loses users at step three, or deciding whether to keep patching a migration that quietly grew over months. The next decision should feel less guesswork-shaped.
Topics here range across AI implementation, SaaS strategy, custom CRM, HR tech, e-commerce, software engineering, data and analytics, design and UX, and domain-specific software for financial markets, TradingView, and real estate. Plus inside stories: short reads on what we learned shipping real products for real businesses.
AI search traffic is mostly invisible in standard analytics. The 5 methods that actually catch it, the 3 that do not work yet, and the stack to build today.
AI search models cite content with original numbers, named authors, and defended positions, structured to be quoted. Generic explainers get summarized away.
Your traffic is about to drop 60 to 80 percent. Your pipeline will not drop with it because AI agents visit on your customer's behalf. The 3 kinds of agent visits, the 5 patterns that make your site agent-readable, and the 4-layer architecture that serves both audiences.
AI search rewards content with original numbers, real stories, and a defended point of view. The sites that get cited carry it. The sites that do not get summarized away.
With limited budget and time, sequence matters. The right order to automate 5 clinic workflows with AI, each one building on the data of the last.
Most clinic voice AI is generic IVR with an LLM bolted on. What makes one actually work is grounding in your calendar, slot rules, services, scripts, and past calls.
AI made answering calls, booking, and reminders cheap to build. Whether they actually work for your patients depends on your own practice data: calls, calendar, records, reviews.
AI made valuations, lead scoring, and market reports easy to build. Whether they are right depends on your own listing, transaction, and behavior data.