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Awesome Martech AI

Awesome

A curated list of AI/ML systems powering modern marketing technology — causal experimentation, user intelligence, ad systems, growth engines, conversational agents, and the emerging Agent-to-Agent marketing paradigm.


Martech AI Stack

Contents

Part I — The Martech AI Stack

Part II — The Agent Era

Part III — Applied and References

Part I — The Martech AI Stack

Introduction

Marketing technology (Martech) has gone through three eras:

  1. Rule era (pre-2015): Segmentation by SQL, journeys by if/then, attribution by last-click.
  2. ML era (2015–2023): Supervised learning for CTR/CVR/LTV, multi-armed bandits for creative, uplift modeling for treatment effects, RL for bidding and budget allocation.
  3. Agent era (2024–): LLM-powered systems that plan, generate, decide, and act across the marketing stack — from creative production to autonomous media buying to conversational selling.

This repository organizes the field into three parts:

  • Part I — The Stack. Six horizontal layers, from Data to Platforms, with the methods and tools that live in each.
  • Part II — The Agent Era. A vertical layer cutting across the stack: marketing agents in production, the four-tier framework that distinguishes them, and where LLM reasoning has structural leverage.
  • Part III — Applied and References. Industry playbooks, original research, books, papers, and community resources.

Entry selection prioritizes deployed systems with disclosed traction, peer-reviewed or production-engineering published work, and material that substantively reframes how practitioners approach a problem.

This list is the marketing-side companion to awesome-quant-ai and recsys-papers.

Stack Map and Design Approach

The Stack

┌──────────────────────────────────────────────────────────────────────┐
│                       Agent Layer (cross-cutting)                    │
│       planning · creative · conversational · media-buying · ops      │
└──────────────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────────────┐
│   Measurement   ←  A/B · Incrementality · MMM · Attribution          │
│   Activation    ←  Paid media · CRM · Push · Conversational · Site   │
│   Decision      ←  NBA · RTB · Allocation · Targeting                │
│   Intelligence  ←  ML · Causal · RL · Embeddings · Foundation Models │
│   Data          ←  CDP · Event stream · Identity graph · Clean room  │
└──────────────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────────────┐
│  Platforms & MLOps (substrate):  Feature store · Experimentation     │
│                                  platform · ML platform · Governance │
└──────────────────────────────────────────────────────────────────────┘

The five core layers are connected by a closed feedback loop: data feeds intelligence, intelligence informs decisions, decisions drive activation, activation produces outcomes, outcomes are measured, and measurement returns training data to intelligence. Platforms and MLOps sit underneath as the substrate. The Agent layer sits on top as a new orchestration tier that lets natural-language goals drive behavior across all five.

Cross-Layer Domain Views

Three terms recur in marketing-AI literature — User Intelligence, Advertising Systems, Growth Engine — that are not stack layers. They are domains that cut across multiple layers. Naming which layers each domain touches resolves much of the apparent ambiguity in the field:

Domain Data Intelligence Decision Activation Measurement
User Intelligence ● ● ●
Advertising Systems ● ● ●
Growth Engine ◐ ● ● ●
  • User Intelligence — builds the system's model of the user. Lives in Data (CDP, identity), Intelligence (LTV, propensity, embeddings), and Decision (audience selection). Recommender systems are a heavy sub-area; see recsys-papers.
  • Advertising Systems — buys impressions in front of users. Lives in Intelligence (ranking models such as DIN, DLRM), Decision (RTB bidding, pacing), and Activation (creative production and delivery).
  • Growth Engine — orchestrates actions to lift business metrics. Lives in Decision (NBA, budget allocation), Activation (channel orchestration), and Measurement (closed-loop experimentation). Often draws on the Intelligence layer (propensity, uplift) without owning it.

A single technique — uplift modeling, for example — can appear under any of the three depending on the question being asked: targeting which users (User Intelligence), which creative (Advertising Systems), or which promotional action (Growth Engine). When the domain term is ambiguous, the resolving question is which layer the decision lives in, not which technique is used.

The rest of Part I is organized by layer rather than by domain.

Design Approach

A defensible Martech AI system is built around the loop, not around a model:

  1. Define the growth objective. Pick one north-star outcome (revenue, retained user months, qualified pipeline). Define guardrail metrics (margin, NPS, brand). Without this, optimization corrupts.
  2. Identify the decision surface. What is the system actually choosing? An audience, a creative, a bid, a message, a timing, a channel mix? The decision determines the method.
  3. Pick the right method for the decision type. Prediction (supervised) ≠ causation (uplift / DiD / synthetic control) ≠ sequential decision (bandits / RL) ≠ generation (LLM). Mismatched methods are a recurring cause of failed projects.
  4. Establish a measurement regime first. Holdouts, geo-experiments, switchback, MMM — pick one before launching, not after.
  5. Build the data contract. Identity resolution, event taxonomy, consent state. The model is downstream of the data contract.
  6. Ship the minimum closed loop. End-to-end coverage beats partial-and-beautiful. A working bandit on three creatives is more valuable than a perfect CTR model with no activation.
  7. Iterate on the bottleneck layer. Most Martech systems stall at a specific layer (often decision or measurement, rarely modeling). Diagnose before adding complexity.
  8. Govern the agent. When LLM agents enter the loop, the constraint is bounded autonomy rather than raw accuracy. Define what the agent may decide, what requires human review, and what is forbidden.

Paradigm Comparison

Paradigm Decision type Data appetite Latency Where it fits
Rule engines Boolean / threshold Low Microseconds Compliance, hard guardrails
Supervised ML Prediction High ~10–100 ms CTR/CVR/LTV, propensity
Causal / Uplift Counterfactual Medium (experiment data) Offline Treatment targeting, incrementality
Multi-armed bandits Exploration vs exploitation Medium ~ms Creative, headlines, subject lines
Reinforcement Learning Sequential policy High + simulator ms–s Bidding, pacing, NBA
LLM Agents Open-ended reasoning + tool use Low (with retrieval) s–min Strategy, creative chains, conversation, Agent-to-Agent

Matching the paradigm to the decision type — prediction vs counterfactual vs sequential vs open-ended — is a recurring source of project success or failure.

Data Layer

The data substrate that everything else stands on: customer events, identity resolution, consent state, and privacy-preserving joins with external data.

Customer Data Platforms

  • Segment — The reference commercial CDP; event collection and downstream routing.
  • RudderStack — Open-source CDP with warehouse-native architecture.
  • Hightouch — Reverse ETL from warehouse to activation tools; the composable-CDP pattern.
  • Census — Reverse ETL alternative to Hightouch.

Identity and Event Schema

  • Snowplow — Open-source behavioral data pipeline; explicit event schemas.
  • Identity graphs and resolution are typically built on top of CDP outputs; vendor offerings include LiveRamp, Adobe RTCDP, and the warehouse-native pattern (BigQuery / Snowflake) with deterministic + probabilistic matching.

Clean Rooms and Privacy

Intelligence Layer

The modeling layer: predictive ML, causal inference, reinforcement learning, embeddings, and foundation models. This is where the marketing system's beliefs about users, items, and outcomes are produced.

Machine Learning Foundations

Causal Inference and Uplift

The discipline that separates Martech AI from generic ML: marketing decisions are interventions, not predictions.

Reinforcement Learning

User Modeling

LTV, propensity, segmentation, embeddings — the user representations that feed Decision-layer choices.

Ranking and Retrieval

The models that score and rank impressions, items, and audiences. Recommender-system models overlap heavily with marketing ranking; see recsys-papers for the full literature.

  • Deep Interest Network (DIN) — Alibaba's attention-based CTR model, deployed in production at scale.
  • DIEN — Sequential extension of DIN.
  • DLRM — Meta's open-source deep learning recommendation model.
  • DCN-V2 — Deep & Cross Network v2 for feature crosses.

Foundation Models for Marketing

Pretrained models applied to tabular CDP data, customer sequences, and creative generation. Early but accelerating.

  • TabPFN — Foundation model for small-to-mid tabular datasets.
  • SASRec, BERT4Rec — Transformer architectures for user-sequence modeling, generalizing toward CDP event streams.

Decision Layer

Given a user model and an inventory, which action does the system take? Bid amount, audience, creative, channel, message, timing.

Bidding and Pacing

Next Best Action

Budget and Audience Allocation

Allocation typically derives jointly from MMM (see Measurement) and uplift targeting (see Intelligence). Tooling listed here covers the optimization step.

  • Robyn — Meta open-source MMM with budget optimizer.
  • LightweightMMM — Google's Bayesian MMM for budget reallocation.

Activation Layer

The channels and surfaces through which decisions reach users: paid media, CRM, push, on-site, and increasingly conversational. The autonomous decisioning inside the major paid surfaces (walled gardens and aggregator networks) is covered in Part II as Class 1; independent agent products operating across surfaces are Class 2; in-conversation service agents are Class 3; the emerging AI-mediated discovery surface is Class 4.

Paid Media Surfaces

The major buying surfaces are Google Ads, Meta Ads, Amazon Ads, TikTok Ads, retail-media networks (Walmart, Target, Instacart), and the open programmatic ecosystem (DSPs, SSPs, exchanges). Each ships with built-in automated decisioning. See Four Classes of Marketing Agents in Part II for the agent treatment.

CRM and Lifecycle Messaging

  • Iterable — Programmable channel orchestration; reference platform for lifecycle messaging.
  • Braze (BrazeAI) — BrazeAI (formerly Sage AI) for AI-driven personalization and journey optimization.
  • Customer.io — Developer-friendly lifecycle messaging.
  • OneSignal — Push and in-app messaging.

Creative Production and DCO

Measurement Layer

How the system knows whether activation worked: experimentation, incrementality, MMM, attribution. The measurement regime determines what can be learned and therefore what can be optimized.

Experimentation Platforms

  • GrowthBook — Open-source experimentation and feature flagging with Bayesian and frequentist engines.
  • Eppo — Warehouse-native experimentation, used by Twitch and DraftKings.
  • Statsig — Feature flags and experiments, free tier for startups.
  • PlanOut by Meta — Origin assignment framework; still relevant for orthogonal experimental design.
  • Optimizely — Long-running commercial experimentation platform.

Switchback and Geo-Experiments

Incrementality

Marketing Mix Modeling

Attribution

Attribution is increasingly treated as a complement to — rather than substitute for — incrementality and MMM. The post-cookie environment has accelerated this shift.

Platforms and MLOps

The substrate that runs across every layer above: feature stores, ML platforms, experimentation infrastructure, governance.

Feature Stores

  • Feast — Open-source feature store; default in many marketing ML stacks.
  • Tecton — Commercial feature platform with real-time path.

ML Platforms

  • MLflow — Open-source experiment tracking and model registry.
  • Weights & Biases — Commercial experiment tracking, evaluation, and observability.

Governance

Data governance, model governance, and consent state management increasingly form a distinct sub-discipline as regulators tighten and clean rooms become primary measurement substrate. Tooling is fragmented; the operational pattern is typically a combination of warehouse-native controls (BigQuery / Snowflake) and a dedicated consent platform (OneTrust, Sourcepoint, Didomi).

Part II — The Agent Era

The new vertical layer that cuts across the Stack: marketing agents in production, the four classes that distinguish them, and the points where LLM reasoning is structurally advantaged.

Four Classes of Marketing Agents

Four Classes of Marketing Agents

"Marketing AI Agent" is applied to four operationally distinct kinds of system. They are not a linear hierarchy — they are categorical classes, distinguished primarily by where the agent operates in the marketing ecosystem. Maturity (incumbent, scaled, PMF, early) is a secondary axis that varies within each class. A long-form treatment, including the buyer-side vs supply-side asymmetry, take-rate economics, and the Chinese-vs-US ecosystem comparison, is in think/marketing-agent-classes.md.

Class 1 — Platform-Owned Automation

Autonomous bidding, creative selection, audience expansion, and pacing built into ad surfaces that own (or aggregate) their own supply. Ships as part of the buying interface; advertisers do not deploy it as a separate product. Built on traditional ML (deep CTR/CVR models, RL bidders, multi-armed bandits); LLMs limited to creative generation. Class 1 manages the majority of global digital ad spend.

Class 1a — Walled-Garden Platforms (own end-user attention)

Class 1b — Aggregator-Network Platforms (aggregate third-party supply)

  • AppLovin (AXON 2.0) — Autonomous ML targeting and bidding inside AppLovin's owned mobile ad network. AXON 2.0 shipped in 2023 and is associated with AppLovin's subsequent revenue and market-cap re-rating.
  • Moloco — ML-driven ad platform for mobile UA and retail-media DSPs; technical reputation comparable to AppLovin in its segments.
  • Mobvista / Mintegral — HK-listed; programmatic ad network with global SSP/DSP infrastructure, strong in Chinese mobile-app outbound.
  • Tencent Ads (腾讯广告) — Autonomous ranking and bidding inside the Tencent superapp surface (WeChat, video, news, games).
  • Alibaba Mama (阿里妈妈) — The same pattern inside Alibaba's e-commerce ad surface.
  • Criteo — Long-running retargeting DSP, historically structured as a network.
  • The Trade Desk — Borderline 1a/1b; independent DSP that matches across exchanges without owning supply, but operates with the same autonomous-buying-surface logic.

The 1a vs 1b distinction matters because economic moats differ: walled gardens own user attention end-to-end and capture the value of automation directly, while aggregator networks must split value with third-party publishers, which limits pricing power but extends reach. Class 1b is nonetheless one of the most profitable corners of the taxonomy — see the long-form essay for why.

Class 2 — Independent Cross-Surface Agents

External agent products sold to brands and agencies. Operate across multiple Class 1 surfaces (Google, Meta, TikTok, retail media, programmatic) without owning supply. Tech substrate varies: scaled players are typically hybrid (traditional ML + LLM creative); early players are typically LLM-native.

  • Albert.ai — One of the earliest autonomous ad agents (originally Adgorithms). Cross-surface management across Google, Meta, and YouTube. Disclosed case: Harley-Davidson reported 5× traffic and a 2,930% monthly lead lift after deployment.
  • Ryze AI — $500M+ ad spend managed across 2,000+ marketers in 23 countries. Reported customer outcome: 3.8× ROAS within 6 weeks.
  • Jellyfish — Agency that replaced parts of its human media-buying team with AI bots. 65% reduction in campaign launch time; for M&S, 80% faster content delivery and 30% cost reduction.
  • Muze AI — YC-backed; positioned to replace $10K–$15K/month agency retainers. 85–90% autonomous; on the Shopify App Store with paying customers.
  • Uplane — YC 2026; profit-aware agency replacement, connects to CRM and ERP to optimize on profit rather than clicks.
  • Absurd — YC 2026; full-stack AI video advertising. Kalshi's "Election Day" spot exceeded a million views.

Class 3 — Conversational and Service Agents

LLM-native agents operating in the post-click conversation: customer support, sales conversations, retention dialogue. Optimize conversation turns and resolution outcomes rather than impressions. This is the class where LLM reasoning is the core product, not a peripheral feature. Economics resemble enterprise SaaS (per-seat, per-resolution) rather than ad take-rate.

  • Sierra — $100M ARR within 7 quarters of founding; co-founded by Bret Taylor (ex-Salesforce CEO).
  • Decagon — AI agents for customer support; significant fintech and consumer-brand traction.
  • Intercom Fin — Intercom's autonomous customer-service agent; deployed across Intercom's SaaS customer base since 2023.
  • Cresta — Real-time agent assist plus autonomous agents for sales and support conversations.
  • Ada — Customer service automation; early LLM-native pivot in the category.
  • Cognigy — Enterprise conversational AI platform for contact centers; strong European enterprise traction.
  • Parloa — European conversational AI for contact centers.

Class 4 — Agent-Mediated Discovery (Frontier)

The newest class. Targets the AI agents that increasingly mediate human purchase decisions — ChatGPT, Claude, Perplexity, vertical buying agents — rather than human end-users directly. No disclosed scale yet; the thesis is structural. Two sub-categories are forming:

Class 4a — GEO / AEO platforms (measure and improve brand visibility inside LLM answers)

  • Profound — Tracks brand mentions and recommendations across ChatGPT, Perplexity, Gemini, and Google AI Overviews; widely cited as the category-defining product.
  • Daydream — GEO platform focused on making brand catalogs and content discoverable to AI buying agents.
  • Scrunch AI — Analytics for how brands appear in LLM-generated responses across major answer engines.

Class 4b — AI-channel ad placement (buying media inside AI-agent surfaces)

  • Lapis — Native ad placement inside ChatGPT; pioneering a new buying surface.
  • sitefire — Agent SEO: making products legible and recommendable to AI agents at the schema/feed level.

As AI agents intermediate more commerce decisions, the SEO/SEM stack must be rewritten. The category is largely empty; LLM understanding is the core weapon.

LLM Agent Leverage Points

Most Class 1 and Class 2 systems are built on traditional ML (gradient boosting, multi-armed bandits, RL bidders), with LLMs limited to creative generation. The optimization loop they run is high-frequency, low-latency, and data-dense, which rewards classical ML over LLM reasoning. LLM reasoning is structurally advantaged in three places:

  1. Strategy layer. Channel mix, market-entry decisions, brand positioning, budget allocation across portfolios. Business-context reasoning rather than per-impression optimization. Class 2 players targeting this layer (Uplane is the clearest example) have a defensible thesis.
  2. End-to-end creative chain. Market insight → creative strategy → copy/visual/video → A/B reading → iterative refinement, run coherently as a single loop rather than as isolated generation steps.
  3. Agent-to-Agent marketing. Class 4 in its entirety — as AI agents mediate more buying decisions, the SEO/SEM stack must be rewritten.

Agent-Building Frameworks

  • LangGraph — Graph-based agent orchestration; common substrate for multi-step marketing agents.
  • Claude Agent SDK — Anthropic's SDK for building tool-using agents; well-suited to strategy reasoning and creative chains.
  • OpenAI Assistants and Responses API — Default for prototyping marketing agents on the GPT stack.
  • CrewAI — Multi-agent role-based orchestration framework.

Frontier (2025/2026)

Cross-stack trends reshaping multiple layers of the Martech AI Stack at once. Agent-class-specific frontiers (e.g., Class 3 expanding into sales conversations, Class 4 product activity) are covered within their respective classes above; this section focuses on shifts that do not map cleanly to a single agent species.

Privacy-First Measurement

Post-cookie, post-IDFA. The revival of MMM, incrementality testing, geo-experiments, and clean rooms as primary measurement substrate. Reshapes the Measurement Layer and the Data Layer simultaneously; affects attribution economics across every agent class. Tooling under the Measurement Layer and Data Layer.

Foundation Models on Tabular CDP Data

Pretrained transformers on event streams and customer behavior — early but accelerating. Watch TabPFN, customer-sequence models analogous to SASRec/BERT4Rec generalized to full CDP event data. Sits in the Intelligence Layer; will change what every downstream class can do with user representations.

AI4AI for Growth

LLM agents that write the experiments, generate the audiences, and propose the creative tests — automating the inner loop of growth itself, not just the execution. Cuts across Intelligence + Decision + Measurement layers. Most YC 2026 marketing-AI cohort entries are bets on some version of this thesis.

Agent-to-Agent Marketing

The structural shift behind Class 4. As AI agents (ChatGPT, Claude, Perplexity, vertical buying assistants) intermediate more consumer purchase decisions, the audience of marketing changes from human to agent. Consequences span every layer:

  • Data: product information must become structured and agent-readable; the schema-and-feed renaissance.
  • Intelligence: embeddings and retrieval must be tuned for agent queries, not human keywords.
  • Decision: the SEO/SEM stack — built on a human-attention model of search — needs rewriting for an audience that does not click.
  • Activation: new surfaces emerge (placement inside ChatGPT, recommendation inside vertical agents); existing surfaces (search, social) lose intermediation share.
  • Measurement: attribution to an agent-mediated purchase is a different problem from attribution to a human-mediated one.

This is bigger than the products listed under Class 4 — it is the largest structural reorientation of the marketing stack since mobile and post-cookie measurement, and it is still mostly upstream of disclosed traction.

Part III — Applied and References

Industry Playbooks

Production case studies and engineering write-ups from companies running Martech AI at scale. Each entry tagged with the primary cross-layer domain it speaks to (UI = User Intelligence, AS = Advertising Systems, GE = Growth Engine).

Original Research and Notes

Long-form analyses written for this repository.

Books

  • Trustworthy Online Controlled Experiments by Kohavi, Tang, Xu — Reference text for A/B testing at scale.
  • Lean Analytics by Croll & Yoskovitz — The growth-funnel framing that still informs modern NBA.
  • Hooked by Nir Eyal — Behavioral mechanics behind retention and lifecycle design.
  • The Mom Test by Rob Fitzpatrick — How to learn what marketing should actually optimize for.
  • Marketing Metrics by Farris, Bendle, Pfeifer, Reibstein — Canonical reference for metric definitions across marketing functions.

Research Papers

Foundational

Recommender Systems

See leoncuhk/recsys-papers for a maintained list covering retrieval, ranking, sequential, and LLM-based recommendation.

Ad Systems

LLM Agents for Marketing (emerging)

Community and Conferences

Communities

Conferences

Related Lists

Contributing

Contributions are welcome. This list is curated, not comprehensive — additions should clear the bar set by existing entries: shipped at scale, published research, or a perspective that changes how a practitioner should think.

  • Prefer open source, published work, or systems with disclosed traction.
  • Disclose affiliation if you built it.
  • One PR per resource; format: - [Name](url) — One-sentence description ending with a period.

See CONTRIBUTING.md for the full guidelines.

If you find this project useful, please consider giving it a star.

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A curated list of AI/ML systems powering modern marketing technology — causal experimentation, user intelligence, ad systems, growth engines, conversational agents, and the emerging Agent-to-Agent marketing paradigm.

github.com/leoncuhk/awesome-martech-ai

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awesome awesome-list recsys causal-inference ai-agents growth-engineering mlops martech marketing-ai llm-agents marketing-agents advertising-systems

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