Customer operations, internal workflows, data and decision systems, and custom AI products. The shape changes with the problem.
getmai.ai is an AI systems company. We start with a real operating problem, map the people, software, data and constraints around it, then build the system that fits that environment.
That distinction matters. We are not a catalogue of four automations and we are not software for one industry. The current site goes particularly deep on clinics, dental, beauty and wellness because those are workflows we already understand well. They are a strong starting point, not the edge of what the company can build.
Most projects fall into four broad areas: customer operations, internal operations, data and decision systems, and custom AI products. A project can sit inside one of them or cross all four. The architecture follows the problem rather than a predefined product menu.
Domain context changes good engineering. A clinic, a retailer and a professional-services firm may all need an AI system, but the workflows, risk, language and integration points are different. Where we already know that context deeply, we say so. Where we do not, we learn it before we build.
Ekaterina Shalel trained in medicine, worked in cosmetology and later built AI products. Before getmai.ai she built SKINBOT, an AI decision layer for beauty retail. That background is useful here for one reason: it combines domain work with product and systems work, rather than treating AI as a demo detached from operations.
The engineers behind getmai.ai have worked together across previous builds. Projects are not sold first and staffed later. Architecture, implementation and integration are treated as one continuous job.
We scope the business problem and the existing environment first. Then we define the architecture, price and timeline, build and integrate the system, test it in the real operating workflow, launch it and hand over the production implementation.
We do not invent ROI percentages, client logos or case studies. We do not force a generic AI product into a workflow because it is easier to sell. And we do not assume every process should be automated. Sometimes the correct architecture keeps a human decision exactly where it is.
Ekaterina Shalel, founder · katyashalel.com
Describe the workflow, what happens today, and the systems involved. We will use that context to decide what should be built and what should stay exactly as it is.
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