SEO vs. LLMO: The same game on a new battleground (A guide for B2B SaaS founders)
You’ve probably heard the chants by now (or read them in overly dramatic Linkedin posts): “SEO is dead! SEO is dead!”
Google used to be the gatekeeper. You wanted customers to find you? You played the SEO game: keywords, backlinks, and even guest posts if you were committed.
Now, the gatekeepers wear a different badge. Their names are ChatGPT, Claude, Perplexity, and Gemini. And they’re quietly re-writing the rules of discovery.
You can call it LLMO (Large Language Model Optimization), GEO (Generative Engine Optimization), or AEO (Answer Engine Optimization), depending on which acronym makes you feel more in with the times. Alphabet soup aside, if you’re a B2B SaaS founder trying to capture high-intent searches, you need to think about how you show up inside an AI answer box.
The real goal of SEO (and why It hasn’t changed)
Here’s the trap: people often equate SEO with traffic. More page_views = success. It's an understandable shortcut, but traffic has always been a means to an end. The real goal of SEO is capturing intent: getting in front of people when they’re actively searching for a solution, comparing vendors, or becoming aware of a problem they’ll need to solve.
That hasn’t changed. What’s changed is where those intent moments happen. Increasingly, they’re not happening in a Google search bar. They’re happening inside LLM chat windows.
So the question is no longer “How do I rank for this keyword?” ; it’s “How do I make sure ChatGPT mentions my product when someone’s in buying mode?”
What do LLMs actually prioritize?
If you want to show up in AI-generated answers, you need to understand how LLMs shape their responses. Based on early research and industry insights, three big patterns stand out:
1. Breadth before depth
Models are trained to start broad. Their default is to produce a neat, safe, listicle answer.
Let's look at an example based on a subject we've worked on a lot before:
Ask “How do I build a live translation engine?” and you’ll first get a broad overview: speech-to-text APIs, machine translation models, video frameworks.
This is why listicles, “best tools for X,” and broad explainers still matter. They’re the content formats most likely to get picked up in a first-pass answer.
2. Depth when users push
When the conversation deepens, LLMs look for authoritative, technical sources.
Example: Ask “How do I build a voice + video live translation engine from raw camera and mic input with 4K output?”
At first, the model explains frameworks. But after one or two follow-ups, it starts surfacing specific vendors and tools: Gladia, Lipitt, Whisper-based APIs.
Translation: You need deep, technical, and authoritative content ready to go. Blog posts, case studies, or even dev docs that prove you’re a trusted source when users dig deeper. (Incidentally, that's why our second SEO project with Gladia focused on hyper-technical subjects that are also commercially relevant; i.e.: content that would only show up in the research of people who are building a voice translation product and looking for tools like Gladia.)
3. Commercial anchors
Here’s the kicker: models aren’t neutral. Perplexity is already experimenting with sponsored follow-ups. And Google’s AI Overviews are designed with monetization in mind.
That means tools, vendors, and APIs will show up when the model thinks there’s a commercial opportunity.
If your brand isn’t structured to look like “product mention material” (clean product pages, integration guides, comparison content), you risk invisibility.
Map the demand path
Traditional SEO playbook:
- Identify a keyword, usually based on interest.
- Build a hub-and-spoke cluster around it.
(Yes, of course there's more to SEO strategy than that, but we can't go too deep into it now.)
LLMO playbook:
Instead of thinking in terms of keywords, think in terms of conversational paths. A single problem statement sparks an exploration, where each follow-up question leads the user deeper into related rabbit holes, until eventually the model introduces potential solutions or vendors.
Take a technical buyer as an example. They might begin with a broad question about how to build a voice and video live translation engine. From there, the conversation branches into increasingly detailed follow-ups — questions about frameworks, latency, GPU optimization, or real-time transcription models. As the discussion unfolds, the LLM starts surfacing specific APIs, open-source tools, and eventually commercial SaaS vendors that can save time and effort.
Your job is to design content that positions your brand along this entire path: broad enough to be cited in the first answer, deep enough to show authority when the conversation gets technical, and structured enough to be recognized as a legitimate commercial option when the model moves toward solutions.
Example flow:
- Query 1 (breadth): “How do I build a voice/video live translation engine?”
- Stage 1: Broad explainers about translation engines.
- Stage 2: Deep dives into ML architectures, APIs, performance trade-offs.
- Stage 3: Vendor mentions (“Here are tools that can save you months of engineering”).
Instead of hubs and spokes, think branching rabbit holes. Every follow-up is an opportunity for your content to be cited.
The role of E-E-A-T in the age of LLMO
Remember E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness)? Google baked it into rankings, but it matters just as much for AI-driven engines.
Here’s why:
- Experience: LLMs surface content that shows firsthand use (e.g., “we built this tool using X API in 2025”).
- Expertise: Author schema, bylines, credentials — models pick up signals of who you are and why you’re qualified.
- Authoritativeness: Citations, backlinks, mentions in other reputable contexts. Still critical.
- Trustworthiness: Clear sources, updated information, consistent brand voice.
Translation: If your content doesn’t scream “written by someone who knows their stuff, in 2025, with proof”, it won’t cut through.
Your tactical playbook for 2025
So what can you actually do this quarter?
1. Format for LLM consumption
When you create content think about how a language model will interpret and reuse it. That means writing self-contained, hyper-specific answers that can stand on their own without context. LLMs also tend to reward structured content, so use comparison tables wherever possible, and make sure your examples feel current by including dates and years (“as of 2025”).
2. Get your technical hygiene right
The basics still matter. Keep your URLs semantic and clean so they’re easy for models to parse. Invest in rigorous schema markup, with special attention to author schema, as models increasingly pick up on who wrote a piece and whether they’re credible. And don’t forget consistency: your author profiles should line up across your website and LinkedIn to reinforce expertise.
3. Deploy LLM-specific tactics
This is where you start playing offense. Add an llm.txt file. Think of it as the LLM equivalent of robots.txt, to guide how models ingest your content. If you already have technical documentation, make it a priority to structure and optimize it, since dev hubs are some of the richest sources LLMs pull from. Treat your docs like “LLM bait.”
4. Match formats to the journey
Finally, adapt your content formats to different points along the buyer’s path. For early awareness, broad content still works best — listicles, “best tools for X” posts, or light explainers. For the middle stage, lean into proprietary benchmarks, case studies, or research that prove your expertise. And at the bottom of the funnel, give models (and buyers) what they need to make decisions: ROI calculators, integration guides, and competitor comparison pages.
So… Is SEO really dead?
Not really. It’s evolving.
SEO as a discipline still matters — but it’s no longer about chasing keywords. It’s about being the brand an LLM can confidently cite.
As Search Engine Land put it: “Embrace the search everywhere mindset.”
People aren’t just searching only on Google anymore. They’re searching on TikTok, Reddit, YouTube, and inside AI chatbots.
Your playbook needs to cover all those surfaces.
The urgency for B2B SaaS founders
Here’s the bottom line:
Brands are going to figure this out fast. The window where you can get ahead with relatively simple LLMO tactics won’t last long, within a year, or less, the playbook will evolve, competitors will catch up, and the bar for showing up in AI answer boxes will be higher. But right now, this is the low-hanging fruit.
If you can make your brand one of the names these models cite in their responses, you bypass the entire Google click gauntlet and land directly in front of people at the exact moment they’re exploring solutions. And for an early-stage B2B SaaS founder, that’s not just vanity traffic or “awareness”. That’s pipeline being built in real time, by the very engines that are shaping how buyers discover products in 2025.