About Xtainable

In regulated work, an answer is only as good as the reasoning behind it.

What would AI look like if it reasoned like an expert you could question?

The Explainable Reasoning Core

How an expert decides, built into a system you can read.

Most real decisions do not have one right answer. An expert weighs the options, picks the one they can defend, and can say why. The Explainable Reasoning Core does the same in software, using fuzzy logic. It works in two parts.

Posture: how it sees

Human reasoning, visualized in clean interfaces.

The judgment of an expert, translated into a system your team can inspect.

Xtainable uses fuzzy logic to make AI reasoning more explainable. Human judgment is rarely exact. One person may call something cold, another may call it mild. One team may see a task as manageable, while another sees a lot of work. Membership functions let us represent those differences clearly, so complex human reasoning can become visible, reviewable, and useful. Our systems are validated by industry experts to ensure that the right things are being considered. Users can always fine-tune or re-tune if desired.

Where does a jalapeño land? It depends on your scale.

To you, this jalapeño is spicy.

Spicy 100%

SensitiveChili lover

The pepper’s heat never changes. Your scale does. Fuzzy logic reads it as a matter of degree.

Transparent Inference: how it decides

Human-readable reasoning, no black-box weights.

Plain IF/THEN rules fire, each as hard as the case calls for. The rules that fire, and how strongly, are the reasoning. So you can read the chain, check it, and change it.

IF criteria-match is strong AND documentation is complete THEN approve
Firing strength
fires strongly
IF criteria-match is borderline AND documentation is incomplete THEN request information
Firing strength
fires weakly

Most-right action

See and modify the reasoning in plain terms.

Every rule that fired, and how strongly, stays on the record. You can audit the chain line by line, and tune any rule when your policy changes.

An EXI: glass box

You get the answer and the reasoning behind it. You can check it, and you can change it.

A black box

You get the answer and nothing else. You cannot check it, and you cannot change it.

Why I Built This

I believe in AI, just not the way it’s being built today.

Most AI today is a black box. It cannot tell you why it reached an answer. A lot of it was built with no regard for the people asked to trust it. Chatbots and large language models guess. You cannot check their work. In a regulated field, that is not good enough.

I am a biomedical AI engineer and a PhD researcher at the University of Cincinnati. My work is on a different kind of AI. It records the reasoning behind every answer, so you can see inside it and check its work. It supports the people already responsible for the decision. It does not replace them.

I built Xtainable to bring that to healthcare, where the stakes are real and trust has to be earned. The standard is simple. Every decision the system makes can be seen and questioned by the people it affects. That is what I am building, one answer you can follow at a time.

Haidar Bin Hamid

Founder & CEO, Xtainable AI

PhD Researcher in Biomedical AI Engineering
AI Bio Lab, University of Cincinnati

Board of Advisors

The people who keep us honest.

Clinicians, researchers, and operators who pressure-test the work. Open a card to read how each one shapes what we build.

Bring explainable AI to your health system.

EXI MED works with the data you already have. Every answer comes with its reasoning. The decision stays with your team.

Request Access →or see how it works