Buyers now ask an assistant instead of browsing, and it names two or three products. Learn what decides whether yours is one of them.
Your traffic is flat, but sales calls keep opening with “an AI suggested you.” Buyers increasingly ask an assistant instead of opening ten tabs, and the assistant comes back with two or three names, a summary of each, and a recommendation. If your product is missing from that shortlist, or described wrongly, nothing tells you — there is no ranking report, no impression count, no click. This course is about earning a place in those answers. You start by recording how five AI models currently describe your product, then fix what they get wrong: answer engine optimization (AEO), writing pages that get quoted rather than merely ranked; structured data, the Schema.org markup that states your pricing, features, and reviews in a form machines read literally; making your product callable by software through an OpenAPI description or a Model Context Protocol (MCP) server; copy that survives being compressed to two sentences; getting listed where assistants look for tools; measuring visits that arrive with no source attached; and a go-to-market plan rebuilt around all of it. Platform-agnostic, mostly no-code.
Built by Lakshya Kumar
Paste this into any AI chat. Fill in the bracketed parts with your context — you'll get back a straight answer on whether this belongs on your plate.
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Sign in to applyFinished the tasks? Take the prompt to your AI and get tested on it. We copy the prompt and open the app — just paste it in.
If an assistant can't use your product without a human clicking through, it recommends the competitor it can use instead.
An AI compresses your page to two sentences before anyone reads it — decide now which two sentences those are going to be.
Assistants choose from a short menu of registered tools, so a product nobody registered was never in the running at all.
Write once for people, for search engines, and for the retrieval systems behind AI answers — without wrecking it for any of them.
AI referrals arrive with no source attached, so your fastest-growing channel is the one your dashboard cannot see.
Every CAPTCHA, texted code, and confirmation click is a place the sale quietly dies when a machine is doing the buying.
Channels, pricing pages, and launch tactics built for human browsing stop working quietly — this is the replacement plan.
Complete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Audit your product's current agent-readiness: test visibility across 5 LLMs, validate structured data implementation, review your OpenAPI/MCP spec (or absence thereof), and score your content for extractability. Then produce an agent-era GTM plan that addresses the gaps — including answer engine optimization plan, structured data roadmap, content rewrite priorities, and a measurement framework using proxy metrics for agent-driven traffic.
I am learning agentic marketing — how AI agents discover, evaluate, and interact with products, and how to make a product visible and trustworthy to agents through answer engine optimization, structured data, agent-callable product design, and agent-era GTM strategy. Help me understand how agents work and how to optimize for them.
Build an LLM-powered outbound agent: researches a target company, identifies a hook, drafts a personalized email, and slots it into a CRM. Test on 20 real targets; evaluate quality (manual scoring), reply rate, and cost per personalized email.
Build a pipeline that generates SEO content at scale: outline from keyword, draft via LLM, fact-check via retrieval, edit via second LLM, human review at quality threshold. Process 20 articles; measure publishable rate and cost per word.
Build an AI SDR: handles inbound lead qualification (BANT-style), schedules meetings, and hands off qualified leads to a human. Test with 50 simulated inbound chats; measure qualification accuracy and human override rate.
Build an evaluation harness for marketing agents: scenario library (50+ situations), automated scoring (LLM-as-judge for quality, programmatic for correctness), and a comparison report across 3 candidate prompts/models. Use it to make a real model-selection decision.
The machine-readable API description standard that makes products agent-callable.