Don’t just track how LLMs
describe your brand. Close the loop.
Discovery Optimization is the unified practice of getting your brand found across algorithmic search, natural-language search, and LLM-based answer engines — and turning every gap into a shipped fix.
Every other tool stops at a dashboard. Rampify produces a pre-filled spec your team or agent ships.
What is Discovery Optimization?
Discovery Optimization is the unified practice of getting your brand found across every surface where someone might ask a question about your category — Google, ChatGPT, Claude, Perplexity, AI Overviews, vertical agents — and wiring each gap directly to the content, technical, and distribution work that closes it.
Users increasingly ask their questions to models, not to search boxes. The mechanisms are different across surfaces but the discipline is one: authoritative mentions on the sources the model trusts, structured and extractable content, accurate indexation, and a consistent narrative across everywhere a buyer might read about you.
It’s not a rebrand of SEO. It’s SEO plus everything that happens after the page ranks — measuring whether the model actually finds you, describes you accurately, and recommends you to the right persona.
Why not just call it GEO, AEO, or AI SEO?
Because those names describe surfaces, not the discipline. GEO targets generative engines. AEO targets answer engines. LLMO targets LLMs. AI SEO is a marketing catch-all. They’re all fractions of the same problem.
The levers underneath are the same: being on sources the model trusts, having structured extractable content, getting indexed, and maintaining narrative consistency. The surfaces will keep multiplying — AI Mode, Perplexity Pages, Copilot, new agents we haven’t heard of yet. A frame that centers surfaces ages badly.
Discovery Optimization centers the outcome — are we found? — which is stable even as the surfaces churn.
The difference is what happens after the dashboard.
Every other visibility tool in the category stops at reporting. Rampify is built as a loop. Research finds a gap. The gap generates a pre-filled spec. The spec ships as content, a technical fix, or a distribution action. The next research session measures whether it worked.
The loop closes in two directions. The obvious one: insight → shipped work. The quieter one: each session teaches the next. New intents surfaced in sub-agent responses feed back as seed prompts. Plans are versioned. Sessions are immutable snapshots you can diff over time. Your Rampify setup becomes unique to your brand — not frozen at vendor-onboarding time.
We give you the edges. You shape what’s inside.
Every other tool in this category is a closed system. Fixed prompts curated by their product team. Fixed personas. Fixed recommendations. Fixed dashboard. You get what they decided matters. Rampify ships the primitives. The research you run, the personas you probe with, the plan you version — yours. The system gets more accurate over time because it gets shaped by your category, your buyers, and what you learn, not by what a vendor assumed.
Composed, not configured
Write your own seed prompts with {{keyword}} and {{competitor}} placeholder expansion. Define personas beyond the Skeptic and Comparison Shopper we ship — ones that match your ICP. Pick the research modes and buckets that matter. The research is yours to author.
Learns over time
Plans are versioned. Sessions are immutable snapshots. Intents discovered in sub-agent responses flow back as new prompts. Your setup sharpens with every run because it’s shaped by results, not frozen at signup.
Auditable trace, no black box
Every query, sub-agent response, tool call, and citation is stored and readable. No aggregate score without the underlying trace. You can check the work — and so can your customer, your developer, or the next agent that needs to act on the result.
A quick note on incentives. Our free tier runs sub-agents on your Claude subscription — we don’t make more money when you run more prompts. Every other tool in this category bills per-prompt, per-month, or per-engine, which gives them an incentive to drive up prompt volume whether or not the signal improves. We don’t have that conflict. If a monthly session on a tightly composed plan is all you need, we’re fine with that.
What makes closing the loop possible.
Three things, underneath: the protocol we’re built on, the methodology we use to measure, and the data graph we sit on.
MCP-native
Rampify is the first Discovery tool an AI agent can call itself. Your coding agent runs the research, reads the results, and files the spec — in the same chat you’re already in. No dashboard switching.
Fresh-context methodology
Every research query runs in an isolated sub-agent that starts with zero knowledge of your brand. That’s the only way a “no mention” signal is trustworthy. Contaminated panels measure how the model treats a brand it’s been told about — the wrong question.
Integrated with the rest of Rampify
Discovery isn’t an isolated tool. Google Ads keyword volume and GSC actual-query strings seed prompts. Business profile + ICP data seeds personas. Indexation state routes gaps. Competitors from the profile expand placeholders. Every output — new intents, new competitors, gap specs — writes back to the same graph.
One graph. One funnel. Every fix routed to the right layer.
Most visibility tools live in a silo. You see a brand-mention trend line and a generic recommendation like “create more comparison content.” But if the page doesn’t exist, if it exists but isn’t indexed, if it’s indexed but doesn’t rank, if it ranks but LLMs don’t cite it, or if it’s cited but the narrative is wrong — those are five different problems with five different fixes. A generic “create more content” recommendation is wrong at least 80% of the time. Rampify sees which layer is broken because it sits on the full graph, and generates the right spec for that specific layer.
What the research actually says.
The category is full of vendor claims. Here are the numbers with sources. Peer-reviewed where possible.
Frequently asked questions
What is Discovery Optimization?
Discovery Optimization is the unified practice of making your brand findable across algorithmic search (Google), natural-language search (Perplexity, Google AI Mode), and LLM-based answer engines (ChatGPT, Claude, Gemini, Copilot), then wiring every gap directly to the content, technical, and distribution work that closes it. It centers the outcome — being discovered — rather than the mechanism of any single ranking algorithm.
How is it different from GEO, AEO, LLMO, or AI SEO?
GEO, AEO, LLMO, and AI SEO are the same problem viewed through different surfaces. Each names a single layer of the discovery stack. Discovery Optimization collapses the stack into one discipline because the underlying levers — authoritative third-party mentions, structured extractable content, indexation, narrative consistency — are the same across every surface. One frame, one data model, one workflow.
What does "closing the loop" mean?
Every visibility tool in the category today reports. You see the dashboard, you write down the gap, you hand it off to a content or engineering team, and the work eventually happens in a different system. Rampify closes that loop. Every gap we detect produces a pre-filled feature spec — with the evidence attached — that a developer or an AI agent can execute directly. The loop runs: research → gap → spec → ship → re-research.
Do I need to replace my existing SEO stack?
No. Discovery Optimization assumes your keyword research, technical SEO, and content workflow already exist. Rampify connects to the same data — Search Console, keyword clusters, your pages — and adds the visibility layer that ties LLM and AI-answer-engine outcomes back to that same system. If you already use Semrush or Ahrefs for keywords and rankings, keep them. We handle the surface they don’t.
What does "MCP-native" mean?
Rampify is built on the Model Context Protocol — the standard AI agents like Claude and ChatGPT use to call external tools. Your coding agent can call Rampify directly to research its own brand visibility, read a research session, or create a spec to fix a gap. No dashboard switching. No copy-paste. The agent does the work in the same chat.
What is "fresh-context methodology"?
Every research query in Rampify runs in a clean sub-agent that has zero knowledge of the brand being researched. A contaminated sub-agent — one already told who you are — produces biased results. Our Phase 1 architecture spawns isolated sub-agents per query, with a persona, a prompt, and a mode (training-only or search-grounded). The result is a measurement of how your brand actually appears to a first-time asker, not a vendor-comfortable narrative.
How does the free tier work?
Sign up with an email, connect your own Claude subscription, and run a Discovery session on your brand. The sub-agents execute on your Claude account, so there are no per-research charges from us. You get two sessions per month, the full diagnostic grid, and shareable results — free forever.
How does Discovery integrate with the rest of Rampify?
Discovery reads from the Rampify graph and writes back to it. Inputs: Google Ads keyword volume and CPC feed seed-prompt prioritization; actual Google Search Console query strings become candidate prompts; your business profile (ICP, competitors, differentiators) seeds personas and {{competitor}} placeholder expansion; indexation state from GSC routes gaps through the funnel so we never recommend more content for an unindexed page. Outputs: new intents discovered in sub-agent responses become keyword candidates, competitors mentioned become tracked competitors, and every gap becomes a pre-filled feature spec in the existing spec system. It’s one graph — not a bolt-on.
Why is the free tier actually free?
Because the free tier runs sub-agents on your own Claude subscription — not ours. We don’t pay LLM costs for your research, so we don’t need to charge per prompt. Every other AI visibility tool bills per-prompt, per-month, or per-engine, which creates an incentive to drive up prompt volume. We don’t have that conflict: if a monthly session on a tightly composed plan is all you need, that’s fine with us.
Related reading
Find out what LLMs say about your brand.
Sign up, connect your own Claude, and run a Discovery session in minutes. The sub-agents run on your subscription — so there’s no per-research cost from us. Free tier forever.