01 · Intent.

Find the prompts shoppers actually ask.

Before you can optimize for AI search, you have to know what shoppers are typing. Step 01 of the RevvUp.ai platform builds a complete intent graph for your category — every high-intent prompt being asked across ChatGPT, Claude, Perplexity, Copilot, and Gemini — and ties each prompt to the specific SKU in your Shopify catalog that should be winning it. Most AI visibility platforms start with measurement. We start one step earlier, because measuring against the wrong prompts gives you a confident score on irrelevant queries.

The problem with starting from your own keywords

The biggest mistake we see in early-stage GEO programs is this: brands take their existing SEO keyword list, run it against AI engines, and assume the resulting score reflects their AI visibility. It doesn't. The queries shoppers use with AI engines are structurally different from the queries they type into Google.

A Google shopper types:

An AI shopper types:

Same intent. Completely different surface. If you measure AI visibility against three-word SEO keywords, you'll miss the seven-filter prompts where the actual buying decision happens. And if you optimize against the SEO keyword set, you'll never crack the high-intent commerce prompts AI engines are surfacing your competitors for.

Intent solves this by building the prompt set the right way: from the AI surface backwards, not from the Google surface forward.

How Intent builds your prompt graph

The Intent step runs three motions in parallel for your Shopify store:

1. Category-level prompt discovery

We start at the category level — the broad commerce queries shoppers use when they're not yet brand-aware. For a beauty brand, that means queries like:

• "best vitamin C serum for sensitive skin"
• "affordable retinol alternatives for beginners"
• "hyaluronic acid serums under $40"
• "clean skincare brands for combination skin"
• "alternatives to Drunk Elephant"

We pull these from the actual prompts being asked across the five major AI engines, not from keyword tools that index Google search behavior. The two datasets overlap maybe 30%; the AI prompt set is where commerce intent actually lives.

For most Shopify beauty brands, this discovery surfaces 800 to 2,100 category-level prompts worth tracking. Pet food typically yields 600–1,400. Supplements 2,000–3,400 (highest density). Apparel 500–900. Home and lifestyle 400–700.

2. Brand-and-product-level prompt expansion

Then we layer in the prompts specific to your brand:

• "Is [your brand] cruelty-free?"
• "[Your brand] vs [direct competitor]"
• "reviews of [your brand]'s vitamin C serum"
• "alternatives to [your brand] under $40"
• "is [your brand] worth the price"

These are the prompts that shape consideration sets once shoppers know you exist. They tend to be where competitive damage happens — your direct competitors get cited inside your own brand prompts unless you've optimized to win them.

3. SKU-to-prompt mapping

The third motion is the part most platforms skip entirely. Every prompt we discover gets tagged with the specific SKU(s) in your Shopify catalog that should be winning it.

That mapping does two things:

• It tells the rest of the platform which SKU to optimize for each prompt
• It makes the work prioritizable by revenue — high-velocity SKUs mapped to high-volume prompts move first

A typical Shopify mid-market beauty brand ends up with 1,400–2,800 prompt-to-SKU mappings after the Intent step. From there, every later step in the platform — Measure, Audit, Fix — runs against this graph.

How we discover the prompts

A few specifics on how the Intent layer actually works under the hood:

Direct AI engine queries. We run a structured query set against ChatGPT, Claude, Perplexity, Copilot, and Gemini directly, refreshed continuously. The prompts we discover from one engine often surface category prompts the others haven't surfaced yet — running all five matters.

Prompt seed expansion. Every prompt we discover gets expanded with the qualifier patterns AI shoppers actually use: budget filters ("under $X"), suitability filters ("for [skin type / breed / size / age]"), exclusion filters ("without [ingredient / material]"), and comparison filters ("alternatives to [competitor]", "[brand A] vs [brand B]").

Community signal. Reddit, niche forums, and category-specific communities give us the prompts shoppers ask each other — which is often what they ask AI next. We monitor the communities AI engines themselves cite (r/SkincareAddiction, r/DogFood, r/Supplements, etc.) for prompt patterns.

Shopify catalog parse. We read your actual product catalog to find SKU-specific facts (ingredients, dimensions, certifications, suitability) and use those to discover the long-tail prompts only your products should be winning.

Continuous refresh. AI prompt patterns shift week over week. New ingredients, new viral products, new comparison brands, new qualifier patterns. The Intent layer refreshes continuously so your prompt graph doesn't decay.

The output: a prompt graph you can use

What you actually see after the Intent step finishes:

• A complete list of category and brand-level prompts mapped to your catalog
• Each prompt scored by estimated weekly volume, commerce intent, and competitive density
• Each prompt tagged with the SKU(s) you should be winning it for
• Each prompt grouped by AI engine — because the prompts that move on Perplexity often differ from the prompts that move on ChatGPT
• A clear ranking of which prompts to chase first, sorted by revenue opportunity

For a typical Shopify mid-market brand, the Intent step surfaces 8–15 prompts worth $5K–$20K/month each in attributable revenue if you can win them. Those become the priority targets for the rest of the platform.

Why prompt intelligence matters more than search volume

Traditional SEO platforms rank keywords by search volume. That works for Google. For AI search, it's the wrong metric.

The right metric is commerce intent per query. A prompt with 200 weekly searches that ends in a purchase decision is worth meaningfully more than a prompt with 2,000 weekly searches that ends in a definition lookup. AI prompts skew much heavier on commerce intent than Google searches do — because shoppers are explicitly asking AI for recommendations, not browsing.

Intent scores every prompt on three axes:

• Volume — how often it's being asked, across the five engines
• Commerce intent — how likely it is to end in a purchase decision (vs research, definition, comparison)
• Competitive density — how many brands AI engines currently name in the response

The highest-leverage prompts are the high-volume, high-intent, low-competitive-density ones. Those are the queries where your brand can break in fast and durably. Intent surfaces them first.

What Intent doesn't try to do

A few things Intent intentionally avoids:

• It doesn't claim to be exhaustive. No platform sees every prompt. Intent aims for the right prompts — the ones that drive revenue — not maximum prompt count.
• It doesn't double-count cross-engine overlap. A prompt asked on three engines is one prompt with three engine-specific scores, not three prompts. This is where many AI visibility tools inflate their numbers.
• It doesn't optimize for vanity prompts. "What is the meaning of [generic term]" prompts get filtered out. Intent's prompt graph is commerce-only.
• It doesn't replace your SEO keyword research. SEO and GEO need separate prompt sets. We run alongside, not instead of, your SEO program.

What happens next

The Intent step feeds directly into Step 02 · Measure. Once you have the prompt graph, Measure scores your current visibility against it — across all five AI engines, with citation, mention, and source rate broken out separately. That gives you the diagnostic baseline the rest of the platform builds from.

Run a free RevvUp.ai audit to see your Intent prompt graph in 60 seconds — no integration, no credit card, just your Shopify URL.

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