AI Search / LLM Visibility Content Strategy

Helping AI tools better understand and surface complex SaaS messaging

 

The Background

As buyers increasingly use tools like ChatGPT, Claude, Perplexity, and Copilot to research software, traditional SEO is no longer the whole picture.

For a complex clinical decision support SaaS product, I led a content refresh focused on improving how large language models understood, described, and surfaced the company in response to relevant buyer questions.

The work combined content strategy, competitive analysis, AI-assisted copy development, and iterative visibility testing to strengthen discoverability in an emerging search landscape.

The Challenge

The company’s website had strong product information, but much of the copy had been written for traditional search and human navigation. It was accurate, but in some places too broad, too internally focused, or not structured around the specific questions buyers were asking.

As AI search tools became more influential in the research process, we needed to understand:

  • Whether LLMs were mentioning the company in relevant category and use-case queries

  • How accurately they described the product

  • Which competitors appeared more frequently

  • What content gaps may have been limiting visibility

  • Which pages needed clearer, more specific messaging

The goal was not simply to “write for AI.” The goal was to make the website more useful, specific, and easier to understand — for both people and the robots quietly judging us.

My Role

I led the content strategy and execution for the AI visibility refresh, including:

  • Auditing how LLMs described the company, product category, and competitive landscape

  • Testing buyer-relevant prompts across AI tools

  • Identifying messaging gaps, vague positioning, and missing proof points

  • Rewriting high-priority web pages for clearer category alignment and buyer relevance

  • Using AI tools to support copy iteration while applying human judgment to messaging, accuracy, and tone

  • Tracking visibility changes over time using AI search monitoring tools

  • Collaborating across marketing, product, sales, and leadership to align messaging with business priorities

The Results

Following the content relaunch, LLM-referred website traffic increased sharply compared with the prior period. Users from AI referral sources increased 210.9% and sessions increased 202.7%, while engaged sessions rose 192.1%.

Most importantly, traffic quality improved. “Get a Demo” button clicks from LLM-referred visitors increased from 1.9% to 15.6%, a 709.1% lift, suggesting the updated content was not only attracting more AI-assisted traffic but also sending visitors to the site with stronger intent.

  • Ranked #1 among tracked brands for AI brand mentions

  • Achieved 60% brand coverage and 40% share of voice

  • Held an average brand position of 1.39 across tracked AI answers

  • Became the #2 cited domain overall across tracked prompts

  • Earned 1,002 website citations across monitored AI responses

  • Increased LLM-referred users by 210.9% after launch

  • Increased LLM-referred sessions by 202.7%

  • Increased engaged sessions from LLM referrals by 192.1%

  • Increased “Get a Demo” clicks from LLM-referred visitors from 1.9% to 15.6%

  • Built a repeatable process for monitoring and improving AI search visibility