We will build that ourselves using AI.

AI is changing the way companies operate. Texts, analyses, and code are generated in minutes. This is a real step forward, and it raises an obvious question: If AI can do so much, is specialized software still necessary?

In the consumer healthcare (CHC) market, we’re hearing this more and more often. With solutions like OTC-Cube, the objection is: “We’ll just program that ourselves, and ChatGPT provides the answers anyway.”

Part of this assumption is true. Another part falls short. This article shows where the line is drawn.

What's true: Surfaces are built quickly

AI drastically lowers the barriers to entry for software development. Today, simple replicas of common tools, internal forms, or dashboards can be created in a matter of days. Anyone who needs an interface to display data can quickly achieve a usable result with AI. This is legitimate and often makes sense.

The real question is: What is displayed on this interface, where does it come from, and who is currently responsible for it? At OTC-Cube, the value lies in data, operations, methodology, and expertise.

Where the Value Lies

OTC-Cube combines software with real-time data collection and industry expertise. The product is built on four pillars:

• Data: 30,000 products from more than 25 sales channels. Prices, availability, and rankings in Germany and Europe. A time series cannot be programmed; it must be measured, week after week. A language model knows neither today’s price at an online pharmacy nor last week’s ranking.

• Operations: Dozens of e-pharmacy channels are constantly changing their layouts and security measures. A prototype is up and running in a week. Ongoing maintenance takes years. Every change to a store can create a gap in the time series. Operations means identifying and closing such gaps before they end up in an analysis.

• Methodology: OTC-Cube tests seven AI engines in parallel and ensures that the same question yields the same results. This requires a rigorous measurement design—not just a clever prompt. What questions are asked, how often, how are they phrased, and how are the answers evaluated? Without fixed rules for this, two measurements cannot be compared.

• CHC Expertise: The consumer healthcare market has its own rules: product data structure, mandatory disclosures, and advertising restrictions. OTC-Cube is designed with these in mind. Errors in the data can have strategic consequences in this market. Those who know the market understand what matters most when it comes to product data.

A prototype answers a question once. A product answers it every week, with the same numbers.

From Measurement to Decision

Numbers alone don't change anything. What matters is what a team does with them. That's why OTC-Cube doesn't provide generic analyses, but rather weekly, prioritized recommendations for action derived directly from the measurement data.

Digital Shelf, AI Visibility, and Content all run on a shared data model. The Content module centrally manages product data, images, and text, has them reviewed using AI, and displays them consistently across all channels. This means that the AI Visibility recommendation is based on the same product data as the price comparison in Digital Shelf. A patchwork of individual solutions would first have to establish this connection. For teams in the CHC market, this means less time spent on Excel spreadsheets and data reconciliation, and more time for decisions regarding product assortment, pricing, and visibility.

What a homemade device would need to do

Anyone thinking about building their own should first consider four questions:

• Who manages data collection when the original developer leaves the company?

• Who verifies that prices and rankings are plausible before they are included in a report?

• Who knows the rules of the CHC market well enough to spot errors in product data?

• Who is responsible for the numbers when management questions them?

These aren't technical questions; they're questions for an organization. A manufacturer in the consumer healthcare sector answers them with brands, product lines, and retail partners - not with software operations. A specialized provider answers them because software, data, and market knowledge are its sole business.

AI cannot verify itself

Ask ChatGPT a product question today and ask the same one again tomorrow. The recommendation may be different: a different brand, a different order. Whether your brand is recommended by AI is therefore something that needs to be measured: systematically, repeatedly, across multiple search engines, and over time.

OTC-Cube’s Visibility module handles this measurement. It shows where your brand is recommended, where it isn’t, and what needs to change. A single result doesn’t tell you much. Only multiple measurements taken over several weeks reveal trends.

What’s more, AI systems are constantly changing. Models are updated, sources are weighted differently, and responses are reformulated. A one-time assessment says little about what will happen next month. That’s why OTC-Cube monitors the data continuously, so that genuine trends can be distinguished from random fluctuations.

Not Against AI, but for Better AI

OTC-Cube doesn’t replace AI—it provides it with verified data. Through Pulse MCP, our interface for AI assistants, the time series are fed directly into your team’s AI tool. Your team asks questions in their familiar environment and receives answers based on verified data, not on assumptions. Same question, same numbers. Anyone using AI in their company needs exactly that: a source that everyone on the team trusts.

AI provides the intelligence. OTC-Cube provides the data foundation you can rely on. If you want to build it yourself, you can. A dashboard can be built in an afternoon. But to run a measurement operation that delivers the same quality every week, you need a team dedicated solely to that task.

Code has become inexpensive. Data, operations, methodology, and expertise have not.