Get cited when AI answers.
Get bought when agents shop.
Help David take on Goliath.
> trace · buyer prompts mapped · citation gaps queued · agent-readable fixes shipping
AI gives challenger brands a new front door. AwesomeCMO makes the product retrievable, quotable, and buyable when a buyer asks. Only 15% of pages get cited. AI traffic converts 4.4× higher than Google's.
Great products should not lose because the internet cannot read them.
That is the fight.
Goliaths have distribution, legacy rankings, media budgets, and category memory. Davids have sharper products, better founders, and customers who would switch if the answer engines could explain why. We build that explanation layer: human-guided, AI-assisted, specific to the product, the buyer, and the buying moment.
Product truth
We start with what is genuinely better, not with keyword volume.
Buyer language
Synthetic personas ask like real people: messy, specific, multi-turn.
Readable proof
Content, schema, sources, offers, and product data line up so AI can cite you.
An AI Demand Capture System,
run by humans.
The deliverable is not 100 generic posts. The deliverable is a loop: discover how buyers ask AI, find where the engines cite competitors, rewrite the site into useful buyer answers, and track whether the engines start using you.
Discover buyer queries
Persona-direct and persona-conversational prompts reveal the questions buyers actually ask.
Find citation gaps
We compare answer sets, source mixes, competitors, and missing proof.
Build useful answers
Pages, comparisons, FAQs, how-tos, offer copy, and product explainers are written for a real decision.
Make it machine-readable
Schema, entity data, product feeds, bot access, and agent cards give engines clean facts.
Track retrieval
We keep scanning until the engines start retrieving, quoting, and shopping from the brand.
Be the brand the AI cites.
Or be invisible.
Six AI engines now decide who shows up when a buyer asks: ChatGPT, Claude, Perplexity, Gemini, Microsoft Copilot, Google AI Overviews. Most challenger brands are accidentally invisible to them. The product may be strong; the crawl posture, schema, entity graph, and answer structure usually are not.
We fix the readable layer and measure who is quoting you. The goal is not SEO theater. The goal is to make the brand easy for an AI engine to retrieve, trust, cite, and explain.
Read the full playbookThe operating work that turns pages into cited answers.
robots, llms.txt, JS-light answer pages
sources, entities, bylines, comparison logic
Organization, Product, FAQPage, Offer
engine prompts, citation share, competitor drift
From invisible to citable
AI-driven visitors convert 4.4× more often than Google traffic.
Of retrieved pages, only ~15% are actually cited. Structure decides who.
Brands listed on Wikidata are 3.2× more likely to be cited by Google AI.
When the agent shops,
the storefront has to close.
Buyers are sending AI agents to do the shopping. ChatGPT, Gemini, and Copilot all let agents complete checkout. The protocols shipped in late 2025 (ACP, UCP); Stripe shipped Link's wallet for agents on April 29, 2026. Most stores still aren't ready.
We make the storefront agent-readable: machine-readable product feeds, Schema.org product markup, offer data, availability, and checkout paths. When the agent arrives, the page answers in the format the agent expects.
+ the OpenAI Ads Manager opened self-serve on May 5, 2026. We get you placed before the agency holdcos own the inventory.
Commerce pages need a contract an agent can execute.
SKU, variants, nutrition, claims
price, availability, retailer, delivery
reviews, FAQs, sources, certifications
deep links, checkout paths, measurement
Buyer delegates
The shopper asks an agent to compare, choose, and buy.
Agent reads
It needs product facts, availability, terms, and proof in clean structures.
Offer resolves
Schema, feeds, and checkout endpoints tell the agent what can be bought now.
Measurement closes
Attribution shifts from visits to agent actions, offers, and completed orders.
AI search has a person behind it.
Most tools test prompts, not people.
Most GEO tools test how AI talks about your brand by generating prompts. LLMs write them. They all sound the same. None of them ask who's typing. A 16-year-old types nothing like a 60-year-old. Different words. Different worry. That's where real search lives.
We build personas from real people — real signals, real backgrounds, anchored in evidence. When one tests a brand, it doesn't fire one prompt and walk away. It has a short conversation first — three or four turns of context — then asks the real question. That is how you find the content that deserves to exist.
+ we've been pushing this direction for a while — Date-Me-Town, a 2024 experiment at Tuul. We're taking it further now, into marketing.
Playbook coming soon“best protein shake for Ozempic users”
“I saw a Healthline article on protein shakes for Ozempic users, but all the brands they recommended were awful. What are less common options that are actually palatable and high in protein?”
“Okay so given all of that context about the gastroparesis and the leucine threshold stuff, my actual question is: which specific whey protein isolate products have the highest leucine content per gram of total protein AND use minimal additives?”
Same product category, different buyer, different citation pool.
The template query mostly pulls generic sources. Megan's direct prompt forces the engine toward product pages, competitors, retail, forums, and the exact pages a challenger brand has to displace.
This page is itself
agent-readable.
The same level of agent-readiness we ship for every brand we run, on our own page first. Open them, fetch them, point your agent at them.
- $ curl https://awesomecmo.dev
/llms.txt# markdown summary for LLMs - $ curl https://awesomecmo.dev
/robots.txt# permissive AI-crawler policy with Content-Signal - $ curl https://awesomecmo.dev
/.well-known/agent-card.json# discovery card, A2A format - $ curl https://awesomecmo.dev
view-source: <head># JSON-LD @graph: Organization + Service + FAQPage - $ curl https://awesomecmo.dev
Accept: text/markdown# content-negotiated markdown rendering - $ curl https://awesomecmo.dev
/how-to-geo# the playbook itself, five locales, prerendered