People are asking AI for answers. Is your infrastructure ready to deliver?

I recently came across a case study showing that traffic from ChatGPT was converting at over 15%, nearly 10x higher than traditional organic search.

That kind of stat is hard to ignore, and it points to a broader shift that’s already underway: people aren’t just Googling anymore. They’re turning to large language models (LLMs) to ask for advice, recommendations and product suggestions in natural language. Because these tools feel so intuitive, users expect them to deliver facts. In reality, some models are trained to predict what’s likely to come next based upon patterns in their training data, not to confirm what’s true. They’re optimized to produce responses that sound right, even when they aren’t always grounded in fact.

The disconnect between how these systems work and how people expect them to behave is where things get complicated. As more people turn to AI for answers, the stakes get higher. It’s no longer just a marketing question about whether your brand shows up in tools like ChatGPT. Businesses are also under pressure to deliver their own AI systems and design them to be helpful, fast and above all, trustworthy. Trust in the output starts with trust in the infrastructure behind it. 

It’s something I’ve been thinking about from both sides of my job: on one hand, I'm watching brands ask new questions about visibility in AI-generated responses. On the other hand, I work closely with teams building AI infrastructure from the ground up. While the marketer in me wants to know how to influence visibility, the product side of me is just as curious about how they actually happen.

What’s really happening under the hood?

LLMs like ChatGPT and Gemini, are trained by ingesting massive amounts of textual data from books, websites, documentation, code and more, and identifying patterns across the data. This training process doesn’t teach the model facts. Instead, it teaches the model how language works: how concepts relate, what words tend to follow others and how to mimic patterns in human communication.

When you give a large language model a prompt like, "What’s the best platform for deploying AI models?" or “Write me a breakup text," the model doesn’t look up the answer in a database. It doesn’t rank a list of web pages. It doesn’t even know anything in the traditional sense. It generates a response. One word (or, more accurately, one token) at a time.

A token is just a chunk of text. It might be a whole word, or just part of one. For example, "deploy" might be one token, but "deployment" could be split into several. Before the model can respond, it breaks your input into tokens, then starts predicting what the next token should be, over and over again, until the response is complete.

That process of taking your input, running it through the model, and generating an output is called inference. This is the operational phase of AI, where the model applies what it has learned from training to a real-world task.

This process is powered by an inference server, the software that runs the model and delivers outputs in real time. If the model is the brain, then the inference server is the nervous system. It doesn’t change what the model knows, but it has everything to do with how that knowledge gets delivered. It manages compute resources, routes requests and determines whether the experience feels smooth and intelligent or slow and frustrating. When the server is underpowered or misconfigured, users may see delays, incomplete outputs or even outright failures. At scale, those milliseconds add up to erode user experience and trust.

What does this mean for businesses?

As natural language becomes the new standard interface, speed, trust and clarity are what move people to action. What used to be a backend decision is now a brand decision. The moment someone interacts with your model, your reputation is on the line, and the underlying technical infrastructure is what determines whether that moment either builds trust or breaks it. 

For organizations that deploy their own models, whether for internal knowledge bases or customer-facing applications, inference infrastructure becomes a critical component of the user experience. A strong setup can efficiently batch requests, allocate GPU memory wisely and scale across traffic spikes. A weak setup bottlenecks the whole system. It’s often the difference between an AI product that feels fast and helpful and one that frustrates users and undermines your brand's credibility.

Flexible, scalable architecture is what gives organizations the confidence to deploy models, adapt to evolving workloads and deliver trusted AI experiences at scale. Open source tools like vLLM are pushing the boundaries even further to enable smarter batching, faster response times and more efficient memory use. When you're delivering near real-time AI to thousands of users, those gains make all the difference.

Your brand’s “front door” is changing.

Information discovery lives in a new pipeline that begins with a prompt and flows through model prediction, infrastructure performance and production delivery. To understand it, we can’t stop at the message. We have to follow the math. And while the marketing world continues to explore how to reverse-engineer visibility in large, black-box proprietary models, there’s a growing opportunity on the technology side to build AI systems organizations control, tailored to deliver fast, reliable and helpful experiences for your prospects, customers and employees alike.

Red Hat AI, a portfolio built to accelerate AI innovation and reduce the operational cost of developing and delivering AI solutions across hybrid cloud environments, is built for this moment. The more performant the infrastructure, the more responsive and scalable your AI-powered experience can be. Whether you’re surfacing your own content via retrieval-augmented generation (RAG) or building a chat assistant that represents your brand, the infrastructure matters.

If you’re exploring how AI can support your business, building a proof of concept with Red Hat AI is the fastest way to get started. It’s a low-risk, high-impact way to test real use cases using your own data and workflows in a secure, controlled environment. You’ll gain measurable results, build confidence in the technology and make informed decisions about how to scale it effectively. Most importantly, you’ll learn what it takes to deliver the kind of answers that your people are coming to expect.