We started by automating clinics, and learned that automation alone is not enough.
Aigenrix was founded in 2025 by Ivan Melnykov, with Ukrainian roots, operating from Spain. We started by solving real operational problems with AI — first in medical practices, where a delayed response has a direct cost. That work kept surfacing the same lesson: if you do not understand the economics first, you risk automating work that should have been redesigned or eliminated instead. If you do not map architecture and data access, automation creates new operational and security risk.
Find the leakage before you build the automation.
Most companies carry a quiet operational cost nobody has quantified. The team is capable and the work gets done — but nobody can say which processes are actually losing money, or whether a given automation project is worth what it costs.
Aigenrix connects the four stages that answer that question under one roof: Profit Intelligence to find the leakage, an Operational Digital Twin to model the cause, Deep AI Automation to redesign the workflow, and Cybersecurity to secure what the automation now touches.
We stay specific on purpose. We proved this pattern first in medical clinics, where speed and accuracy both matter and a generalist AI cuts corners no one can afford. Every stage we have added since holds to that same standard.
AI that improves the economics of the business, not just its software.
The goal is not more AI. The goal is a better-performing business. An AI system that costs more to run, review and secure than the leakage it fixes is not a win — it is another line item.
We measure against that standard on purpose: a documented baseline, an honest estimate of the effect, and a payback date, not a percentage that sounds good in a slide.
The technology exists. The hard part is delivery — making it work inside a real business, on real data, without creating a new risk somewhere else. That is the work.
Who delivers this
A small, founder-led team by design. You work directly with the people who do the work — not a layer of account managers.
Ivan Melnykov
Works at the intersection of finance, business operations and AI — identifying where businesses lose money, understanding the operating causes, and determining where AI can create measurable economic value. Combines financial analysis, management experience and AI implementation so Aigenrix projects start with business economics rather than technology.
We started Aigenrix building AI for medical clinics, because every one we worked with was running the same broken operations — lost messages, paper processes, weekend silence. Clients kept asking the same next question: what is this actually worth, and who is checking what the agent can see? Financial diagnosis and security stopped being someone else's problem to hand off, and became the rest of the job.
Other things we build
Deep AI Automation is the layer we started with. These are smaller side projects we ship out of our own product muscle.
Want to know where your business is leaking, and what it would take to fix it?
30 minutes. We map your biggest friction points and tell you honestly what is worth doing first — and what is not.