Revelancy
AI is becoming the front door of shopping: people ask it what to buy, and it chooses which brands get named. Revelancy is the company I founded to get brands into those answers.
THE SHIFT
People bring the entire shopping process to AI now: the vague first question, the follow-ups, the head-to-heads, the final gut check before buying. Somewhere in that conversation the model reads the whole category and narrows it to three or four names, and everything it leaves out is gone from the decision entirely. That is already how millions of people shop. Next comes the buying itself, with every major platform building toward checkout that happens inside the conversation. And the way brands are built, from product data to content to measurement, still assumes a human is doing the searching. I started Revelancy at the front of that shift, in the space the industry calls AEO and GEO, where the playbooks are still being written.
WHAT HAD TO EXIST
Three things had to exist for any of it to work. A method for diagnosing a brand and fixing it. Something to keep that method from going stale in a field that moves weekly. And an operating model that could actually deliver all of it.
PART 01
THE METHODA five-platform diagnostic across ChatGPT, Perplexity, Google AI Mode, Gemini, and Claude: an eight-dimension query bank built from the questions real buyers actually ask, split by buying versus research intent, run in repeat passes on every platform because the models don't answer the same way twice. Two numbers come out: whether the models know a brand at all, and whether they recommend it when nobody has said its name. The gap between those two is the diagnosis.
Measurement says where a brand stands. The Revelancy AI Visibility Blueprint decides what to do about it: it crosses what a brand actually is against what each platform rewards against which competitors already hold the ground, then sorts every signal a brand could compete for into one of six moves. Close, invest, amplify, expand, defend, or drop it, because sometimes the models have already reached a verdict on the category and no budget moves it. Telling a brand where to stop is usually worth more than another checklist.
Structured data, product feeds built for the AI shopping surfaces, crawler access, and product content rebuilt for how models extract and cite. It lands as a package a developer deploys in an afternoon.
Models trust what the internet says about a brand more than what the brand says about itself, so the other half of the work happens off-site. The Brand Authority Playbook covers it: nine plays across publications, reviews, entity data, video, and community, each ranked by how strong the evidence behind it actually is, then sequenced against the gaps the diagnostic found. The sharpest targets are the articles AI already cites in the category; the brand just isn't in them yet. I map it, template it, and hand it to the brand's team to run.
PART 02
THE RESEARCH ENGINEAI shopping changes weekly, which means a methodology built once is wrong within a quarter. The Adaptive Research Engine keeps the rest of it current, and it is a build in its own right: a full intelligence pipeline with its own rulebook.
Agentic monitoring scans the space twice a day, monthly deep research passes dig underneath it, and platform announcements and primary studies land as they publish. Nothing enters the methodology on the strength of appearing somewhere: every source is classified by rigor and independence, academic work weighted differently from vendor research and differently again from a practitioner's blog post, and every finding is tagged for evidence strength, dated, and re-ranked when something better arrives. Claims that fail are logged so they cannot resurface wearing a new citation. Over 250 findings sit in the intelligence base across six domains, and when one contradicts what the system currently believes, that contradiction triggers a formal update pipeline that traces the change through every document it touches.
PART 03
THE DELIVERY MACHINEDelivery runs as a production line: every stage has its own specification and structured handoff, so an engagement moves from research to diagnosis to strategy to finished deliverables without losing context between steps. AI runs the execution passes on a library of production prompts and templates, and browser automation and API integrations handle data capture across all five platforms and the handoffs in between.
Quality is gated twice. An automated audit checks every deliverable for factual accuracy and structural consistency, down to whether each recommendation still traces to the strategy that produced it. What the audit can't catch is a correct fact stated at the wrong confidence, so the last pass is always a full read and sign-off from me before anything ships.
THE REST OF IT
STATUSBeyond the three builds: positioning and pricing, the sales and outreach structure, contracts, a Postgres layer for audit and benchmark data, over a hundred system documents, and the brand and the site.
I parked Revelancy by choice: right now the pull is to go build inside a team. The research engine still runs, the system stays current, and all of it is ready to switch back on.