Now open in offline mode (no live sessions) due to high demand — enroll anytime and earn the same certificate.
Mastering Marketing for AI Native Products
A practitioner-first curriculum for marketing AI-native products — owning the answer in GEO/AEO, designing for machine and agent buyers, building an AI-native marketing stack, and shipping a full go-to-market plan.
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About this program
Search is becoming answer-based, not link-based. Buyers — both enterprise and consumer — increasingly delegate research and purchasing to AI agents acting on their behalf. Content production cost has collapsed toward zero, and legacy attribution dashboards break down in a zero-click world of autonomous, minute-by-minute decisioning. This cohort covers the complete practitioner stack needed to market an AI-native product in 2026: owning your presence in AI answers (GEO/AEO), designing for machine and agent buyers rather than only humans, building an AI-native marketing stack instead of bolting AI tools onto campaign-era workflows, producing content and creative at machine speed without losing brand judgment, hyper-personalization on first-party data in a cookieless world, PLG funnel mechanics built around experiential proof, dedicated B2B and B2C deep dives, measurement and attribution when clicks stop telling the truth, and the organizational and skillset shifts marketing teams need to make. Every module covers both B2B and B2C playbooks — not a generic one-size-fits-all curriculum — and the course ends with a capstone: building and shipping a complete AI-native go-to-market plan for a specific product and buyer. It runs in offline mode — no live sessions, enroll anytime, and work through all 12 modules and 45 lessons at your own pace, ending with the same verifiable certificate as our live cohorts.
Who is this for?
Marketers, growth leads, founders, and product marketers who need to market AI-native products — where search is answer-based, buyers delegate to agents, and content production is nearly free
What you'll actively build & learn
Understanding Fundamentals
Grasp the core mechanics of AI systems, from transformers to retrieval algorithms, moving beyond superficial APIs.
Production-Ready Architecture
Learn how to architect scalable, resilient generative AI applications that handle edge cases and high throughput.
Hands-on Engineering
Write custom PyTorch models, build multi-agent swarms using LangGraph, and deploy to Kubernetes.
Verifiable Execution
Complete rigorous capstone projects that serve as a proof-of-work portfolio for your next AI engineering role.
Time Commitment & Schedule
Self-Paced Modules
Flexible
No live sessions — work through all 12 modules whenever suits you, in any order you need.
Hands-On Labs
~20 hrs total
Every module ends with a lab: AI brand audits, agent-legibility rewrites, PLG teardown, capstone GTM plan, and more.
Module-Based Syllabus
Each module is structured around three things: what you'll cover, what capability you'll walk away with, and the concrete deliverable that moves you toward a working system of your own. Work through them in any order, at any pace.
12 self-paced modules, 45 lessons — work through them in order or jump to what you need
A complete, shippable AI-native go-to-market plan for your own product, plus fluency in GEO/AEO, agentic buyer journeys, and machine-speed content production
Practitioner-first lessons covering both B2B and B2C in every module, ending in a hands-on capstone
Foundations: Positioning
- Translating product capabilities into buyer outcomes — the foundational framework used throughout the rest of the course.
A repeatable framework for positioning an AI-native product around outcomes rather than features.
A Capability → Outcome translation map for your own product.
GEO / AEO — Owning the Answer
- Why the click is dying, how LLMs decide what to cite, and how to build and monitor an AI reputation layer.
The ability to diagnose and improve whether AI systems cite, recommend, and accurately characterize your product.
A full AI brand audit across major answer surfaces (Module 2 capstone lab).
Marketing to Machines
- The rise of the machine customer, designing for agent legibility, and winning both B2B vendor comparisons and B2C agentic shopping flows run by AI agents.
An understanding of how to make your product legible and preferred by agents acting on behalf of human buyers.
An agent-legibility audit and rewrite of a key product/comparison page.
The AI-Native Marketing Stack
- The three pillars of AI-native marketing operations, GPS-navigator thinking versus campaign-era thinking, multi-agent systems, and the 2026 tool landscape.
The ability to distinguish genuinely AI-native marketing operations from teams that have simply bolted AI tools onto manual workflows.
A stack audit mapping your current tools against the three-pillar architecture, with a build-vs-rent recommendation.
Content & Creative at Machine Speed
- The new content economics now that production cost has collapsed, AI video and multi-modal production, and where human judgment still wins.
The ability to run rapid iteration cycles on content that matters instead of chasing volume for its own sake.
A "brief once, ship everywhere" content system applied to a real campaign brief.
Hyper-Personalization & First-Party Data
- Building on a cookieless foundation with zero-party data and consent, plus B2B ABM and B2C behavioral segmentation.
A first-party data and consent strategy that supports durable, legal AI-powered personalization.
A zero-party data collection and lifecycle segmentation plan for one funnel stage.
Funnel Mechanics — PLG, Trials & Show Don't Tell
- Why experiential proof beats landing-page persuasion for AI products, PLG architecture and the aha moment, and B2B/B2C expansion and virality mechanics.
The ability to design a product-led funnel where the first output moment does the persuasion work.
A PLG funnel teardown and redesign centered on the aha moment.
B2B Deep Dive — Selling AI to Skeptical Enterprises
- The 2026 enterprise AI buying committee (including the AI procurement agent), ROI narratives, the dark funnel, and building a trust packet.
The ability to map a buying committee and build the specific proof assets each member needs to say yes.
A complete Trust Packet for an enterprise deal.
B2C Deep Dive — Identity, Magic & Distribution
- Consumer psychology of AI adoption in 2026, immersive try-before-you-buy, and influencer, creator, and community-led distribution.
An understanding of what moves a skeptical-but-capable 2026 consumer from trial to habitual use.
A creator/community distribution plan paired with an immersive trial experience design.
Measurement & Attribution
- Why legacy dashboards break under autonomous, high-frequency decisioning, attribution in a zero-click world, predictive models, and governance guardrails.
A measurement framework built for a world where agents shift budgets and creatives thousands of times an hour.
A governance and rollback plan for an autonomous budget-shifting or bidding system.
Org Design & the New Marketing Skillset
- Moving from a relay-race org to a control-room model, the new skill stack, auditing "boring" processes for automation, and responsible-automation ethics.
The ability to redesign a marketing workflow around AI-native roles instead of bolting tools onto legacy job descriptions.
A process audit identifying the highest-leverage workflow to redesign, plus a role-evolution plan.
Capstone — Build & Ship an AI-Native GTM Plan
- Defining a specific product and buyer, building the full GTM stack, a 90-day launch calendar, and a final agent-readiness test.
A complete, specific, shippable go-to-market plan rather than a generic strategy deck.
A capstone AI-native GTM plan with a 90-day launch calendar, submitted for the Agent Test.
The syllabus builds toward a final proof of work.
The weekly syllabus is designed to stack toward a capstone that demonstrates what you can actually build. By the end of the cohort, you are not just finishing modules. You are presenting a concrete output that ties the learning arc together.
View Alumni CapstonesIndustry-Grade Certification
Earn a credential that actually matters. Every certificate is tied to your Capstone Project repo, valid for life, and optimized for your professional technical profile.
View Certification TiersYour instructor
Anubhav Srivastava
Anubhav has spent the past two decades building machine learning and AI systems across startups, large enterprises, and high-scale consumer platforms. He has worked on patented AI technologies, authored books, and founded multiple ventures, and is currently building a deeptech startup focused on physical AI. Known for combining technical depth with practical thinking, he enjoys breaking down complex ideas into clear, accessible insights and is driven by a curiosity for how technology can solve real-world problems.
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