How discoverability works

Keywords are the words people type into Google. It's tempting to chase the biggest, most obvious one — a winery might want to rank for “wine.” But the huge words are impossibly competitive and full of people who aren't buyers. “Intent” means the reason behind a search: someone typing “wine” could be a student or a shopper — you can't tell. Someone typing “how to fix a rejected COLA label” is telling you exactly what they need, and they need it now. Those specific, high-intent phrases are far less contested and far likelier to turn into a customer. You find them with keyword tools (like SEMrush or SpyFu) and Google Search Console, which shows the real phrases already bringing people to your site.

Example — COLAClear: targets phrases a producer types mid-problem — “COLA prescreen tool,” “how to fix a rejected COLA label” — instead of the hopeless, generic “wine labels.”

Search engines and AI build a picture of who you are by matching mentions of your name across the web — your site, directories, social profiles, press. Every time your name or web address shows up in a slightly different form, the machine can't tell it's all the same business, so your credibility gets split into several weak, half-recognized versions instead of one strong, trusted one. The fix is boring but powerful: pick exactly one business name and one web address and use them identically everywhere — same spelling, same capitalization, same link.

Example — Example: a startup that showed up online under three different names and web addresses at once taught search engines it was three faint, unrelated things instead of one credible company. Standardizing to a single name and address is often the single highest-leverage fix.

A normal web page shows words and pictures that people understand but machines have to guess at. Structured data is a bit of invisible code you add to the page — following a shared vocabulary called schema.org — that spells things out for machines: “this is an Organization, its name is X; this is a Product; here's a customer review.” When search engines and AI can read that, they stop guessing and can describe you accurately — and you become eligible for the richer Google results (star ratings, FAQ drop-downs) that stand out.

Example — COLAClear: includes schema.org tags labeling it an Organization and a SoftwareApplication, plus its founder and a customer review — so an AI can correctly restate what the tool does, who built it, and that it's well reviewed.

“LLM” means Large Language Model — the technology behind AI assistants like ChatGPT, Gemini, and Claude. llms.txt is a simple text file you place at the root of your website, written specifically for those AIs. It's a short brief: who you are, what you do, and the key facts you want them to get right. Think of it as handing the AI a cheat sheet instead of hoping it pieces your story together correctly from scattered pages. It's a newer, still-emerging convention, but it's cheap to add and it directly shapes how AIs summarize you.

Example — COLAClear: publishes an llms.txt stating its scope and the exact regulations it checks — the same facts that later showed up, correctly, in Google's AI answer.

“Crawlers” (or “bots”) are the automated programs search engines and AIs send out to read the web. A small file called robots.txt on your site tells those bots which ones are allowed in and which pages they may read. Many sites, often by accident, block the newer AI crawlers — and if an AI's crawler can't read your page, it can never quote or recommend you. Being cited by AI requires an explicit open door: your robots.txt has to welcome those crawlers in.

Example — COLAClear: deliberately allows the AI crawlers (the ones behind ChatGPT, Google's AI, and others) to read and quote it.

People increasingly search in questions — “why did my label get rejected?” — and AIs answer questions for a living. If your site is organized around the real questions your customers ask, with a clear, direct answer under each, you're handing search engines and AIs clean, quotable passages that map exactly to what someone asked. A good FAQ page and a plain “how it works” page are quiet workhorses — among the most-cited kinds of pages there are.

Example — COLAClear: has a 16-question FAQ and a methodology page built around the exact questions producers ask — ready-made answers for both Google and AI.

Internal links are links from one page of your site to another. They do two jobs. For visitors, they guide someone who lands on one page toward the next useful thing. For search engines, links are how crawlers travel and how “authority” (the trust a page has earned) flows between pages — a well-linked site signals which pages matter and how they relate. Left to chance, your best pages sit stranded; done on purpose, your FAQ feeds your articles, which feed your product page.

Example — COLAClear: deliberately links its FAQ, its articles, and its tool to each other, so a visitor or crawler who lands anywhere is guided to the rest.

Authority isn't something you can claim about yourself — it's earned by consistently publishing things worth reading, and by being cited by sources others already trust. A “content engine” is a repeatable habit: turn what you know, or the data you have, into genuinely useful articles on a regular cadence, and place bylined pieces in the trade press your audience reads. Over time that steady output is what makes search engines and AIs treat you as a credible source rather than a random site.

Example — COLAClear: runs ongoing industry analyses and has bylined articles in Wine Business Monthly, Wine Industry Advisor, and Craft Spirits Magazine — the content engine behind its rankings.