From SEO to LLMO: How to make AI recommend your products and services

Search traffic is shifting to "Answer Engines" and if LLMs can't understand your value proposition, they won't recommend you. Here’s my 3-step process to fix this.

Welcome to issue #83 of FutureBrief. Three times a week I share practical insights on AI & automation trends, tools, and tutorials for business leaders. If you need support on your technological journey, join our community and get access to group chat, Q&As, workshops, and templates.

When I ask potential clients how they found out about Ninjabot, the answer used to be predictable. They’d say they clicked an ad, got a recommendation from a friend, or saw a post on social media.

But in the last couple of months, I’ve noticed a new, distinct trend.

More and more people are telling me they found our work through ChatGPT.

They didn’t Google us. They didn’t click a Facebook ad. They simply asked an LLM for a solution, and the AI recommended us.

This is the biggest shift in business discoverability since 2005.

If your website is still built just for Google keywords, you are slowly becoming invisible to the smartest potential clients in your market.

The “Search” vs. “Answer” economy

For fifteen years, the way clients found you was simple. They’d search “marketing automation agency” on Google, scroll through ten blue links, click five of them, and make a choice.

That world is gone.

The flow starts to look completely different. A potential client now asks ChatGPT: “Who are the top three automation agencies for a mid-sized dental practice? Compare their pricing and specialization.”

The AI doesn’t give them a list of links to browse. It analyzes its training data, runs a live search on Perplexity, and recommends exactly three specific businesses with reasoning for each.

The client contacts those three. If you aren’t on that list, you don’t exist. You don’t get a click, you don’t get a chance.

That’s even more brutal than good old search on Google.

BrightEdge research shows, that AI referrals to e-commerce websites are up 752% year-over-year. Perplexity is processing over 100 million queries a month, and OpenAI recently launched ChatGPT Shopping.

The gatekeepers have changed. And if you’re still optimizing for 2015’s search engine, you’re invisible to 2025’s buyer.

Why AI sees your business differently than Google

Think of Google as a librarian.

It indexes keywords. It scans the library for text strings like “marketing agency,” “London,” or “affordable” and hands you a list of everything that matches.

AI is different. AI is a consultant.

It doesn’t just match words. It understands intent, context, and reputation. And that changes everything about how you get found.

Imagine your current website says:
“Leading marketing solutions for modern businesses. 10 years experience. Contact us.”

To Google, that’s fine. It sees the keywords. It sees your domain authority and backlinks. It ranks you.

But to an AI consultant looking for a specific solution, that description is meaningless. It’s too vague to recommend.

Now imagine your website says this:
“Specialized marketing automation for dental and medical practices ($2M-10M revenue). Certified HubSpot Platinum Partner. 50+ documented case studies. Average ROI 300% in 6 months. Pricing starts at $2,500/mo.”

The difference?

Google might ignore this because the keywords are too niche.

But ChatGPT, the consultant, sees gold. It sees a specific answer to a user’s specific problem. When a user asks for “the best agency for a dental practice,” the AI recommends you not because you ranked, but because you fit.

The 3 Layers of LLMO (LLM Optimization)

Layer 1: The who & what

The first problem is that AI models hallucinate when data is vague. They only recommend you with confidence when your data is specific.

Look at your headline. If it says, “We help companies grow,” the AI ignores it. It’s meaningless noise.

Instead, rewrite your core value proposition to be hyper-specific: “We help B2B SaaS companies with $1M-10M ARR reduce churn by 15% using customer success automation.”

You need to explicitly state who you serve (e.g., “Mid-market logistics firms”), the problem you solve (”Reducing supply chain latency”), and your methodology (”Using n8n and custom Python scripts”).

Layer 2: Trust signals

LLMs prioritize information that looks like a “fact.” Unstructured testimonials are just marketing fluff, but structured case studies are data.

Check your “Results” page. Is it just a collection of quotes saying “Client X loved working with us”? That’s invisible to an algorithm.

Change it to structured data: “Client X (Logistics) saved 40 hours/week and $12k/month. Implementation time: 3 weeks. Tools used: Slack, Airtable, OpenAI.”

To make this machine-readable, use Schema Markup (Organization Schema). Just like e-commerce sites, service businesses need JSON-LD code to tell AI: “We are a LocalBusiness,” “We serve area X,” “Our price range is $$.”

Finally, publish deep-dive content. If you write the definitive guide on “Dental Practice Automation,” the AI cites you as the expert when answering questions about that topic.

Layer 3: The digital footprint

AI doesn’t just trust your website, it checks your reputation by looking for confirmation across the web.

Search for your business on Perplexity. If only your website appears, you have a trust problem. You want it to cite your LinkedIn, a Clutch review, a guest post, and a Reddit thread.

To fix this, get listed on structured directories like Clutch, G2, and Crunchbase, as AI models rely heavily on these sources for “best of” lists. Encourage detailed reviews that mention specific outcomes, like “They fixed our CRM integration issues in 2 days,” which gives the AI specific tokens to match with future queries.

And don’t ignore human discussions: Reddit and Quora are massive training sources. A positive mention in r/smallbusiness is weighed heavily as a “social proof” signal.

The 60-minute LLMO action plan

Here is the exact step-by-step framework that I use to improve how AI sees my businesses. And it takes less than an hour to implement it.

Step 1: The prompt test