SEO as we know it isn’t dead yet, but it’s terminal.
The future of visibility is GEO: Generative Engine Optimization. And GEO is powered by one thing:
Your AI trust graph.
A trust graph is the invisible web of signals that tells large language models (LLMs) like ChatGPT, Claude, and Gemini how credible you are, what you specialize in, and whether you deserve to be pulled into an AI-generated answer.
But let’s not be theoretical. Below is exactly how a health tech SaaS analytics marketer can shape that AI trust graph strategically, step by step.
LLMs don’t treat your website as “the source.”
They treat your brand as an entity — and that entity is defined by:
Think of it as your company’s public reputation, turned into AI math.
For a growth-stage analytics SaaS company the question AI asks is:
“Do trusted outside sources validate this brand as real, authoritative, and accurate?”
AI asks: Do credible people talk about you? Earned media = trust currency. It’s the fastest trust builder. If SEO is the resume, earned media is the reference check.
For a growth-stage analytics platform, this could mean:
Example:
You publish new data on reducing no-show rates by 23% using predictive analytics. Healthcare IT News covers it. A CIO from OhioHealth comments. That coverage becomes a high-value node in the trust graph.
AI cares about who you’re “standing next to.” Entities get trust from adjacency.
For a healthcare SaaS analytics company, you could:
Every association becomes a new line in your trust graph.
Example:
You collaborate with Duke Health on a small outcomes analysis. Duke shares the findings in their innovation newsletter. Now you’re linked in the model’s graph to “Duke Health,” “academic research,” and “patient outcomes.” Authority skyrockets.
AI rewards companies that stay in their lane and go deep. LLMs seem to be big believers in the line 'jack of all trades, master of none.' Don’t choose 25 different topics. Choose a few and stay focused. LLMs love high-value, low fluff, factual content that is expert-driven, specific, and numerically grounded.
AI rewards companies that stay in their lane and go deep. LLMs seem to be big believers in the line “jack of all trades, master of none.” Don’t choose 25 different topics. Choose a few and stay focused. LLMs love high-value, low fluff, factual content that is expert-driven, specific, and numerically grounded.
For healthcare analytics, you can:
The more consistent your company is, the easier it is for AI to “lock in” what you’re an expert in.
Example:
You continually publish real-world metrics on denial prevention, capacity optimization, and patient throughput. The model recognizes: you = analytics for operational efficiency.
LLMs check for consistency across time. They don’t want to cite a flash in the pan as a source.
They want:
This is where growth-stage companies often struggle — they can pivot as often as every 6–12 months.
But AI heavily favors consistency. This is why core messaging is so important.
Example:
You keep core messages steady for 18 months: “We improve operational capacity using predictive analytics.” Over time, this consistency strengthens the entity graph. A competitor who keeps rebranding from “AI predictive analytics” → “data automation” → “workflow platform” fragments their trust graph.
High-value experiential signals include:
This is where health tech SaaS stands apart — you naturally generate data.
Use it.
Example:
You release anonymized aggregated benchmarks showing:
– 31% reduction in appointment lag
– 24% improvement in throughput
– 19% reduction in denials
These stats are cited by a trade publication, which then becomes a high-authority node the model trusts.
The bottom line
For the next decade:
Every earned placement, every expert quote, every consistent data point becomes a new node in your authority network—and the companies who start building now will own AI visibility long before their competitors even understand why.