Interview with Monica Tomasso, AI Visibility Expert, Founder, Monic AI Systems

This interview is with Monica Tomasso, AI Visibility Expert, Founder, Monic AI Systems.

For readers meeting you for the first time, can you introduce yourself as the Founder and AI Visibility Expert at Monic AI Systems and share how you help organizations show up inside AI-generated answers?

Hi, I’m Monica Tomasso, founder of Monic AI Systems.

What I focus on is helping businesses show up in a place most teams are not paying attention to yet, which is inside AI-generated answers.

Right now, more people are asking tools like ChatGPT, Perplexity, or Google AI who they should hire or what they should choose, and they are getting a single answer instead of a list of links. If your business is not included in that answer, you are effectively invisible in that moment.

That is where I come in.

I help organizations shift from thinking about rankings and traffic to thinking about how they are actually understood and selected. That means making sure their expertise is clear, consistent, and visible across the web in a way AI systems can recognize and trust.

In practice, it is less about producing more content and more about making sure the right signals exist so your business can be chosen when it matters.

Building on your definition of AI visibility, when you audit a brand’s presence in AI search (e.g., ChatGPT, Claude, Perplexity), what are your first 30-day steps?

When I audit a brand’s presence in AI search, the first 30 days are focused on understanding how the business is actually being seen today.

1. The first step is simple. We run a set of real buyer-style questions across tools like ChatGPT, Claude, and Perplexity to see if the brand shows up at all, and how it is described when it does. That gives a very clear baseline.

2. From there, we look at where those answers are pulling from. In most cases, it is not the company’s website; it is third-party sources, profiles, or scattered content across the web. That is where gaps start to show.

3. Next, we focus on clarity. Many businesses are not consistently described across platforms, which makes it harder for AI systems to understand what they actually do. We tighten that up so there is a clear, consistent signal.

4. Then we identify what is missing. Usually that means content that answers real buyer questions, not just general marketing content, and making sure it exists in places beyond the website.

By the end of the first 30 days, the goal is not perfection; it is visibility. We want the brand to start appearing, even if it is not yet the top recommendation.

Once that baseline is established, we can start improving how often and how confidently the brand is selected.

For small and mid-sized businesses with limited resources, what is the minimal viable setup you recommend to become machine-readable to AI systems?

For small and mid-sized businesses, the goal is not to do everything. It is to make sure the basics are clear enough that AI systems can understand and trust what you do.

The minimal setup starts with clarity. Your website and your Google Business Profile should clearly and consistently describe who you are, what you do, and who you serve. That sounds simple, but a lot of businesses are surprisingly vague or inconsistent across platforms.

Next is structure. You want a few key pages that directly answer real customer questions, not just general marketing copy. Clear service descriptions, simple FAQs, and straightforward explanations go a long way.

Then comes presence beyond your site. Even a small number of consistent mentions on other platforms, directories, or profiles helps reinforce that your business is real and credible.

I’ve seen businesses improve their visibility just by tightening these three things, without adding more tools or content volume.

You do not need a complex system to start. You need clear signals that are easy for both people and AI systems to understand.

We often do this through structured conversations that capture a founder’s expertise.

You’ve talked about using an agentic content system anchored by an AI-powered podcast—how do you operationalize that workflow so one interview becomes distributed, structured signals that LLMs reliably recognize and cite?

At a practical level, we operationalize this through a coordinated set of AI agents rather than a manual content process.

Each agent handles a specific part of the workflow. One focuses on breaking down a conversation into the real questions a buyer would ask. Another turns those into clear, structured answers. Others handle connecting and reinforcing those pieces so they do not sit in isolation.

The output is not a single article. It is a set of tightly related pages and FAQs that all point to the same core idea, written in a way that is easy for both people and AI systems to understand.

We then make sure those signals exist in more than one place. When the same ideas show up consistently across a small number of platforms, it becomes much easier for AI systems to recognize and trust what the business represents.

The important part is that this is not a one-time process. The system tracks whether the business is appearing in AI-generated answers and adjusts the language and structure over time.

That is what makes it scalable. One conversation turns into a connected set of signals that can be refined based on what is actually working.

How do you quantify “AI rankings” and visibility across LLMs and chat search in an executive dashboard that leaders can trust for decisions?

We treat the dashboard as a decision system, not just a reporting tool.

At the executive level, it answers a simple question: Are we being seen, recommended, and ultimately chosen by AI systems?

That shows up as three core signals: whether the brand is seen in responses at all, whether it is recommended as a credible option, and whether it is selected as the top choice.

Behind that, there is a much deeper layer of measurement.

We track a defined set of high-intent prompts based on what buyers are actually asking, and we monitor how the brand appears across those queries in tools like ChatGPT, Claude, Perplexity, and Gemini.

From there, we measure things like total mentions, share of voice against competitors, sentiment, citation quality, and how consistently the brand shows up across multiple models. We also break visibility down across the buyer journey, from early problem searches through comparison and hiring intent.

Two areas leaders find especially useful are model consensus and memory versus real-time presence: one shows whether multiple systems agree on recommending you, and the other shows whether you are being surfaced live or actually remembered over time.

The key is that none of this sits in isolation. Every metric ties back to something the business can influence, whether that is how it is described, where it appears, or how consistently its expertise shows up across the web.

So, while the surface view is simple, the underlying system gives leaders confidence that what they are seeing reflects real buyer behavior and can be acted on.

Shifting to chatbot optimization, which specific training data choices and human-in-the-loop workflows have most improved answer accuracy, safety, and conversion in the chatbots you’ve deployed?

While this is often framed around chatbots, the bigger impact comes from how AI systems, in general, generate and select answers about a business.

What has made the biggest difference is being intentional about what the system learns from and keeping a human in the loop where it matters.

On the training side, we focus less on volume and more on clarity. The most effective inputs are structured examples of how the business actually answers real questions. That includes transcripts, FAQs, and real customer conversations. When the system learns from how the business naturally explains things, the responses become far more accurate and aligned.

We also prioritize clean, unambiguous definitions of what the business does and does not do. That reduces drift and improves safety because the system is not trying to guess.

On the human side, we do not try to remove people from the process. We place them at key points.

One is at the beginning, shaping the source material so the system is learning from the right inputs.

Another is in review loops, where we look at how the chatbot is actually responding in real scenarios and refine the language over time.

We also monitor how conversations convert: where people drop off, where they ask follow-up questions, and where they move forward. That feedback is critical because it shows where the answers are clear versus where they are creating confusion.

The biggest shift is treating the chatbot less like a tool and more like a system that improves over time. Accuracy, safety, and conversion all improve when the inputs are grounded in real expertise and there is a feedback loop that keeps refining how it responds.

Drawing on your buyer intent and A/B testing background, how do you design and run experiments that increase inclusion in AI-generated recommendations without resorting to low-quality content?

We design experiments around one outcome: increasing inclusion in real AI-generated answers without lowering the quality of what the business is saying.

It starts with real prompts, not assumptions. We track the actual questions buyers are asking across tools like ChatGPT, Claude, and Perplexity, and use those as the foundation.

From there, we test variations in how clearly and directly those questions are answered. This means not different topics, but different ways of expressing the same expertise. That might include how specific the answer is, how it is structured, and how closely it aligns to the language used in the prompt.

We then observe which versions are picked up and included in AI-generated responses and which are ignored.

The key is that we are not experimenting with filler content. Every variation is grounded in real expertise and a clear point of view. If something feels generic, we discard it quickly.

Over time, patterns emerge around what gets included. We scale those patterns while maintaining the depth and specificity that make the content valuable.

So the balance comes from testing expression, not substance. The expertise stays the same. We are refining how clearly it is understood and selected.

Given your interests in education, health, and social impact, what low-cost 90-day playbook would you give a nonprofit to increase AI visibility and trust without a big budget?

For a nonprofit with limited resources, I would focus on a simple 90-day playbook built around clarity, structure, and consistency.

1. Days 1–30: Capture and clarify your expertise

   Start by recording a few structured conversations with your team about your mission, your programs, and the questions you get most often. This is one of the most overlooked steps, and also one of the most powerful. It gives you real language to work from instead of trying to create content from scratch. From there, make sure your website and profiles clearly and consistently describe what you do, who you serve, and why it matters.

2. Days 31–60: Turn that into structured content

   Take those conversations and break them into the actual questions people ask. Then turn those into a small set of clear pages and simple FAQs that answer those questions directly. The goal is clarity, not volume.

3. Days 61–90: Reinforce those signals beyond your site

   Make sure those same answers show up in a few additional places, such as partner sites, profiles, or contributions. Consistency across even a small number of sources helps AI systems recognize and trust your organization.

You do not need a large budget. You need a way to turn what you already know into signals that can be understood and reused.

Thanks for sharing your knowledge and expertise. Is there anything else you'd like to add?

If there’s one thing I would leave people with, it’s this:

Most businesses are still focused on how they show up in search results. But increasingly, decisions are happening before someone ever sees a list of links.

AI systems are becoming the place where people ask direct questions and expect a clear answer. In that moment, there are no rankings—only a few options are surfaced.

So the shift is not just about being visible; it’s about being understood well enough to be selected.

Organizations that adapt to this will not just be easier to find—they will be the ones people are told to choose.