Patients and clients don’t just Google anymore — they ask ChatGPT, Perplexity, and Claude “who’s the best dentist near me?” or “which firm handles this kind of case?” If your practice isn’t the answer those tools give, you’re invisible to a fast-growing slice of demand. I built PracticeRank to fix that — an AEO and SEO platform that gets local practices found everywhere people search, and I’m an equity owner in the company.
The problem PracticeRank solves
Local practices — dental offices, medical clinics, law firms — live and die by being found. For twenty years that meant Google: rankings, the map pack, reviews. That still matters, but a second front opened up almost overnight: Answer Engine Optimization (AEO) — being the practice that AI engines name when someone asks for a recommendation.
Most agencies are still selling 2015-era SEO and have no answer for AI search. I built PracticeRank to own both.
What is AEO (Answer Engine Optimization)?
AEO is the practice of making your business the answer an AI engine gives — not just a blue link on a results page. When someone asks ChatGPT or Perplexity “who’s a good family dentist in my neighborhood?”, the model returns a short list of names and a sentence of reasoning. AEO is the work of making sure your practice is on that list, described accurately, and cited from a source the model trusts.
It overlaps with SEO but it isn’t the same discipline. Classic SEO optimizes for a ranking algorithm that returns ten links. AEO optimizes for a language model that returns one answer assembled from many sources — your site, your reviews, directory listings, structured data, and third-party mentions. The signals that make you “the answer” are different: clean structured content the model can parse, machine-readable facts about your services and location, consistent information across the web, and a footprint the model has actually ingested. Get those right and you show up in the response. Get them wrong and you don’t exist in that channel, no matter how well you rank on Google.
How to get your practice found in ChatGPT and other AI search
The question I get most from practice owners is simple: how do I actually show up when someone asks an AI for a recommendation? Here’s the short version of what PracticeRank automates.
First, make your site legible to models. That means real structured data — LocalBusiness, FAQ, Review, and procedure schema — plus llms.txt and llms-full.txt files that hand an AI a clean, authoritative summary of who you are, what you do, and where. Most sites give a model a pile of marketing prose to guess from; a legible site hands it the facts directly.
Second, publish answer-first content built around the questions real patients and clients ask. AI engines pull from pages that directly answer a question in the first sentence, not from pages that bury the answer under three paragraphs of throat-clearing. If someone asks “does this dentist do same-day crowns?”, the practice whose site answers that question plainly is the one that gets named.
Third, keep your facts consistent everywhere — Google Business Profile, directories, citations, reviews. Models cross-reference. Conflicting hours, addresses, or service lists make you a less trustworthy source, and less-trusted sources get left out of the answer.
Fourth, measure it. You can’t improve what you can’t see, and almost nobody is watching whether AI engines mention them. PracticeRank queries the major engines for the searches that matter to each practice and tracks whether — and how — they get named over time.
AI search for local business is the real edge
Traditional SEO is a crowded, commoditized market. AEO is not — it’s new, it’s where attention is moving, and almost no one selling to local practices knows how to do it. For a local business, that’s an unusual window: the channel where your future patients and clients are increasingly starting their search is one your competitors don’t even know how to compete in yet.
Building PracticeRank around being the answer AI gives — not just ranking on page one — is what makes it different, and it’s the same philosophy I bring to every product: get ahead of where demand is going, not where it’s been.
What I built
PracticeRank is a full platform, and I built the tooling end to end:
- AI-visibility tracking across five engines. The platform queries ChatGPT, Claude, Perplexity, Gemini, and Grok for the searches that matter to each practice and tracks whether — and how — they get mentioned. You can’t improve what you don’t measure, and almost nobody was measuring AI visibility.
- Automated technical SEO + AEO. Schema markup (LocalBusiness, FAQ, Review, MedicalProcedure),
llms.txtandllms-full.txtfor LLM discoverability, sitemaps, robots, and site audits — generated and published automatically. - Content that ranks and gets cited. Buyer-intent, answer-first content built from each practice’s real search data and published to their site via CMS APIs.
- Local SEO + reputation. Google Business Profile optimization, citation building, neighborhood pages, and review automation that turns happy patients into steady five-star momentum.
- A monthly agent. A containerized, AI-powered agent runs per customer to keep everything fresh — new content, updated schema, gap analysis, and reporting.
The whole thing is designed to automate 85%+ of the work so it scales without an army of account managers.
Why I built the tooling myself
There’s a reason PracticeRank is a platform and not a services shop with some spreadsheets. The only way to help a lot of local practices — and to keep the quality high while doing it — is to encode the expertise into software instead of renting it out an hour at a time. Agencies that run everything by hand hit a ceiling: every new client needs another account manager, and quality drifts as the team grows.
So I built the tooling end to end, from the AI-visibility tracking to the schema generation to the monthly agent that keeps each customer’s site fresh. That’s the leverage. A practice owner gets work that would take a specialist team, delivered by a system that does the repetitive 85% automatically and reserves human judgment for the parts that actually need it. It’s the same instinct behind every product I build: find the expensive, error-prone, manual work and turn it into software that does it reliably at scale.
It also means the platform gets smarter over time rather than just bigger. Every practice we track teaches the system more about what actually moves AI visibility in a given market, and that compounds in a way a purely human agency never can.
Frequently asked questions
What’s the difference between SEO and AEO? SEO optimizes for search engines that return a ranked list of links. AEO optimizes for AI engines that return a single synthesized answer. The goal of SEO is to rank; the goal of AEO is to be the answer. They share some foundations — good content, structured data, a consistent web presence — but AEO leans harder on machine-readable facts and on being a source the model actually trusts and has ingested.
Does AEO replace SEO? No — it’s additive. Google still drives enormous volume, and much of the technical work that helps AEO (clean structure, strong content, consistent citations) also helps traditional rankings. PracticeRank runs both at once so you’re not trading one channel for another.
How long does it take to show up in AI answers? It varies by market and starting point, which is exactly why measurement matters. The honest answer is that it depends on how legible your site is today, how consistent your presence is across the web, and how competitive your category is. That’s why the platform tracks visibility continuously rather than promising a fixed timeline.
Who is PracticeRank for? Local practices that live on being found — dental offices, medical clinics, and law firms — especially owners who know Google still matters but can see that patients and clients are starting to ask AI tools for recommendations.
The takeaway
PracticeRank is proof of a pattern I care about: take a real, high-stakes problem (local practices going invisible in AI search), build the tooling to solve it properly, and automate it so it scales. If you’re building an AI product and want that kind of technical leadership — from architecture to launch — that’s exactly the work I do as a fractional CTO.