Business is ready for AI, but doesn't know where to start. How UNIDATALAB solves clients' business problems — case study

UNIDATALAB is an artificial intelligence company with its own research and development center, which integrates AI tools on a turnkey basis. Due to the white-label principle, no one will know that the business has used this cheat code.

UNIDATALAB told us how they work, and also shared a case of cooperation with an international brand in the field of oral care.

AI can do more

According to the Exploding Topics service, 78% of global companies use artificial intelligence. However, this does not mean that all of them have moved beyond the startup stage. For many, the «base» is the hype around ChatGPT, creating texts or images in Midjourney, recording and transcribing meetings, as well as deep research to obtain analytics for various tasks.

AI can do more. In particular, work with more complex concepts and solve large-scale problems. UNIDATALAB is engaged in precisely such tasks. Why?

In practice, most companies' path to AI looks the same. First, interest and budget appear. Then — experiments with ChatGPT, text or image generation. Then — attempts to apply these tools to more complex product tasks. And it is at this stage that the process usually stops.

Businesses quickly encounter the limitations of universal tools. They do not take into account the specifics of the product, do not integrate into the existing logic of the service, and do not answer the main question — how will this solution affect metrics and revenue. If the answer is not obvious — the idea is rejected.

As a result, AI is perceived as an expensive experiment with an unclear outcome. Decisions are postponed «until better times,» with which businesses lose momentum and competitive advantage.

For progress to happen, it’s worth considering whether you’re using AI at the solution level, or mainly at the tool level?

Oral care brand case

When the partner approached UNIDATALAB, the client already had an AI request and a budget allocated. The company understood that AI could be a growth point, but did not have a clear vision of which solution to integrate.

Discussions started with abstract ideas—personalization, analytics, recommendation mechanics. But none of them answered the key question: how would it impact the product and business metrics?

It was at this stage that it became clear that the client needed not a separate AI feature, but a holistic product concept. The solution had to quickly demonstrate business value, drive regular user engagement with the brand, and create the basis for a new monetization model. At the same time, it had to be a long-term tool with the potential to scale.

The AI ​​team joined the work at the pre-sale stage. In a white label format, it worked together with the partner, helping to translate the general request into specific product logic.

It wasn’t just about the technical implementation. It was about explaining why this particular solution made sense, how it would scale, and what business impact it could have. It was this work—between business and technology—that became key to the client’s decision.

Thus, a mobile application was born, which creates daily interaction with the brand and has real value for users. The functionality covers everyday requests and is tailored to the client’s products. The application can assess the condition of teeth based on a photo, give personalized care recommendations, and also remind you when it’s time to replace your toothbrush and which model to choose.

Personalized recommendations, progress tracking, and gamification elements encourage regular returns to the app, creating a stable brand presence in the user’s daily routine.

What are the end results?

The integration of the AI ​​solution gave the client a full-fledged user interaction tool that works on several levels simultaneously — from product to business.

Increased engagement and frequency of contact with the brand. Thanks to personalized mechanics, the application encourages the user to be regular. A habit of interaction is formed, trust in the company’s recommendations increases, and the risk of switching to a competitor is minimized.

Creating a new monetization channel:

• An environmental incentive to replace your brush when it’s time.
• Increasing the frequency of repeat purchases.
• Customer LTV growth.
• Increasing loyalty to the brand ecosystem.

At the same time, monetization occurs organically and unobtrusively — due to the benefit for the user.

Strengthening the brand’s image as innovative. The launch of the application allowed the company to:

• To consolidate the image of a technologically progressive brand.
• Emphasize expertise in oral care.
• Go beyond classic FMCG positioning.

And for the partner, for whom UNIDATALAB became a temporary in-house team, this case significantly strengthened the image and guaranteed long-term cooperation with the client.

More specifically, the partner:

• Expanded the service stack without the need to form a separate internal team.
• Was able to offer the client a comprehensive product solution with an AI component.
• Increased expertise in the eyes of the customer.
• Received a long-term contract, the ability to scale the solution, scope for additional stages of product development, and stable cooperation with the client built on trust.

UNIDATALAB working principles

The approach to work and service are worth no less than the final results of cooperation. UNIDATALAB believes that partnership should bring satisfaction to all parties — from communication to intensive joint work.

The team works only with AI solutions — without branching out into related areas. This is what drives deep expertise and helps keep the focus on the goal.

The company operates in a white label format — without a public presence, but with full involvement in the partner’s processes. For the end customer, the team looks like part of the partner’s internal expertise.

The desire to work long hours. Not for the number of hours, but for a result that will exceed the client’s expectations.

AI is not an end in itself. Every solution is tested by a simple question: what business problem does it solve? If the answer is questionable, the idea is killed.

The work model is built so that the client receives maximum expertise without unnecessary costs. If one experienced expert and a helper are enough to solve the problem, then so be it.

Win-win. The customer wins because they achieve their business goals and earn more. The team wins by creating a product with real impact and also makes money.

To reap the benefits of AI, it is important to understand where it creates value, how it fits into the logic of the product, and how it affects business results.

The main conclusion of the above is that successful AI solutions are not born in code, but in the right combination of strategy, expertise, and partnership.