Yesterday, I wrote about the two-person AI marketing stack that does the work of 20. The most common reply was a version of the same question: okay, but what’s actually in the stack? People wanted the map, not just the vision.
Here’s the map. Category by category, with the tools I actually use, what they cost, and how they connect to each other. This is the full AI marketing stack that replaces a traditional mid-market marketing department in 2026.
Why the map matters
Most marketing-stack diagrams you see online are either aspirational (built by vendors selling you something) or archaeological (built by agencies documenting what they already sold you). This is neither of those. It’s the stack I run on client engagements and on my own work. Every tool earns its place by doing real work, and every category has a clear lead.
You don’t need all of it. But you do need to know which category is which, so you can decide what to build first and what to defer. Most teams I talk to over-invest in automation tools before they have a content engine worth automating, or pay for enterprise analytics before they know what question they’re trying to answer. A good map sorts that out before you spend the money.
The stack, category by category
1. Strategy and thinking
Lead: Claude
This is where positioning docs, creative briefs, messaging hierarchies, and strategic memos get written. Claude’s long context window (200k tokens) means a full discovery-call transcript, competitive audit, and a pile of customer interview notes fit in one conversation. Output is proportional to what you load in. The teams getting the most out of this layer brief Claude like a new hire on day one rather than typing questions into it like a fancy Google.
What I use it for weekly: brand voice refinement, strategic memos, ICP work, first-draft creative briefs, analyst-level research synthesis.
Cost: Claude Pro at $20/month. If multiple operators are using it heavily, Claude Team at $25/seat.
2. Content production
Leads: ChatGPT + Claude (for different modes)
Claude writes long pieces where voice matters. ChatGPT generates volume: 40 ad headlines, 20 email subject lines, 10 value-prop variants in a single prompt cycle. Claude handles the taste work. ChatGPT handles the volume.
Both get amplified through Projects (Claude) and Custom GPTs (ChatGPT). Every recurring format — blog draft, LinkedIn post, case study, cold email — gets its own Project or GPT, pre-loaded with the brand guide, voice samples, and format spec. Drafting starts with context baked in. I wrote about the seven prompts I use weekly; each one lives in a Project by now.
Cost: $40-50/month combined for both services.
3. Visual content
Leads: Midjourney + GPT-Image 2 + Nano Banana + Higgsfield
Image tools split by job. The breakdown from the image model comparison is the one I still run:
- Midjourney for editorial illustration, hero images, anything that needs to look like a design studio made it
- GPT-Image 2 for in-context edits, ads with readable text baked in, quick iterations
- Nano Banana (via Gemini) for fast volume, where you can generate 20 variants while you wait
- Higgsfield for short-form talking-head and founder-face video
Four tools sounds like a lot. In practice it’s a single set of monthly charges and a habit of knowing which tool owns which job.
Cost: $90-150/month total across all four.
4. SEO and research
Lead: Ahrefs, with an LLM layer on top
Ahrefs is the backbone for keyword discovery, competitive gap analysis, and rank tracking. It has been for a decade, and the core product barely needs to change. What’s new is layering Claude or ChatGPT on top. Feed 40 competitor articles into a conversation and ask for patterns and missing angles. A day of analyst work compresses into an hour.
The 2026 development is generative engine optimization (GEO), or getting cited inside ChatGPT, Claude, and Perplexity answers. This is where the next several years of compounding traffic will live for brands that get it right early. Ahrefs has started adding GEO features; they’re early but worth tracking.
Cost: Ahrefs Lite $129/month.
5. Automation and agents
Leads: Zapier for mainstream plumbing, Gumloop for LLM-in-the-middle flows
Zapier does the boring glue: CRM sync, form-to-Slack, calendar triggers. Gumloop picks up where Zapier stops, handling workflows where an LLM analyzes something mid-pipeline and passes the result forward. For anything proprietary — a weekly competitor-deck refresher, an inbound-lead enricher, a blog-post distribution chain — I reach for custom agents built on OpenAI’s or Anthropic’s APIs.
The discipline: automate what drains your best people, and leave the rest alone. Over-automating is how marketing teams end up with a graveyard of half-working Zaps that break quietly while nobody notices.
Cost: Zapier from $19.99/month, Gumloop from $97/month. Custom agents are pay-as-you-go API usage.
6. Analytics and reporting
Lead: GA4 + Search Console + Claude for interpretation
No LLM replaces a clean dashboard. The LLM’s job is interpretation. Every Monday, I pipe last week’s numbers into Claude and ask three questions: what changed materially, why, and what to do about it. It catches correlations and pattern shifts a human analyst would take a full afternoon to surface.
For paid media, the ad platforms’ native reporting is fine for most engagements. Only step up to something heavier when you’re running more than three channels at real volume.
Cost: GA4 and Search Console are free. Claude usage stays within the Pro plan from category #1.
The total
At the maximum, running this full stack for a small marketing operation costs between $300 and $600 per month. That’s a rounding error against the headcount it replaces. For a lean two-person team, you’re spending roughly $15-30 per working day on total tooling.
How the tools connect
Think of the stack as a workflow graph rather than a grid of separate apps. Three handoffs matter most:
Strategy → Content. Positioning docs and creative briefs written in Claude get pasted into ChatGPT Custom GPTs as the upstream context. Every piece of content is downstream of a brief that was already AI-processed.
Content → Visual. The hero image prompt for every blog post is generated in Claude from the post’s own content. The prompt goes into Midjourney or GPT-Image. The brief is usually the bottleneck, not the writer or the designer.
Everything → Analytics. Content, ads, and email all feed GA4 and Search Console. Claude reads the data. The weekly pattern: look at the numbers, ask Claude what changed, let the answer inform next week’s priorities. Most weeks the loop closes in under an hour.
How to build your version
Don’t build the whole stack on day one. Build it in sequence.
Week 1: Claude Pro + ChatGPT Plus. Nothing else. Get comfortable with both. Write one custom GPT for your most-repeated content format, and stop there.
Week 2-3: Add one image tool: Midjourney or GPT-Image. Pick based on what you produce most. Editorial content leans Midjourney; ads with readable text lean GPT-Image.
Month 2: Add Ahrefs. Layer Claude on top for competitor synthesis.
Month 3: Add Zapier (not Gumloop yet). Automate one workflow that eats ten hours a week, and leave it at that for a month before adding a second.
Month 4-6: Depending on what you’re shipping, add Nano Banana for ad volume, Higgsfield for founder-face video, or Gumloop for LLM-in-loop flows. Anything that doesn’t earn its keep inside 30 days gets killed.
The common failure mode is buying the full stack up front and getting overwhelmed before any of it earns its place. The teams producing real output built their stack over months, tool by tool.
Why the operating rhythm is the real asset
The stack is the operating system for a marketing function, not a list of tools. The tools are interchangeable. What actually produces work is the operating rhythm: strategy informs content, content informs visual, everything informs analytics.
If you’re staring at a half-built stack and feeling like you’re falling behind, you’re not. You’re probably ahead of most teams, who are still in the “should we try AI” phase. The real question is which two tools you’ll commit to this month.
Work with us
If you want help building this stack (mapping what you actually need, picking the right two starting tools, getting the operating rhythm running), that’s exactly what we do. We take on a small number of engagements per year.
