A Profit Intelligence company, focused today on margin.
Aigenrix helps businesses with roughly €1M–€30M in annual revenue see which customers, products, projects or processes may be eroding margin, test that against evidence, and act only on what is economically worth fixing. Founded in 2025 by Ivan Melnykov, with Ukrainian roots, operating from Spain.
Find probable economic loss. Test it against evidence. Measure what is realized.
Many companies carry an 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 stages that answer that question: the Margin Leakage Diagnostic, a fixed-scope test of where margin is lost, built on the Profit Intelligence methodology; implementation of only the changes the evidence justifies, with pricing, policy and process changes first and the Operational Digital Twin, AI & Workflow Automation or Security & Governance where they are justified; and measurement of the result that actually materialized.
We started by automating clinics and learned that automation alone is not enough: if you do not understand the economics first, you risk automating work that should have been redesigned or eliminated. That lesson is why every engagement now starts with the economics, and why we stay specific on purpose.
Better economics, not more software.
The goal is not more technology. The goal is a better-performing business. A system that costs more to run, review and secure than the leakage it addresses 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.
Evidence before opinion
- →Every monetary figure traces back to your data and states whether it is calculated, estimated or measured.
- →Suspected losses are tested before they are reported; rejected hypotheses are reported too.
- →AI helps search and process evidence. It is not trusted to decide what is economically true on its own.
- →Judgment calls on business meaning, causality and feasibility are made by people with finance and operations experience.
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 — testing where businesses may be losing money, tracing it to operating mechanisms, and determining where technology, including AI, is economically justified. 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
AI & Workflow 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 margin is going, and what the evidence supports doing about it?
We start by establishing whether there is a credible economic case worth analysing — and tell you honestly if there is not.
30 minutes, free and without obligation. We look at your situation and decide together whether a paid next step makes sense.