Our Research & Technology

“Health advice must be based on high-quality science. This is not a luxury. At the moment, false evidence and big players’ opinions overrule and ignore results.”

Democratizing health intelligence

Ancestralize collects all relevant scientific knowledge, integrates it into a coherent knowledge base, and uses software intelligence to generate advice. It is capable of probabilistic reasoning to find the diseases and pathologies, most likely given the observed symptoms, laboratory results, and risk factors. Our software provides immediate advice at a fraction of the usual healthcare prices.

Our Research Posture

Many people assume medical and nutrition advice is “settled” and that the majority view is always correct. We don’t. In these fields, popular ideas can stick around even when they’re incomplete, outdated, or shaped by intense financial and social pressures. We also think medicine often focuses too much on managing symptoms instead of asking, “What’s driving the problem underneath?”

That’s why we put a lot of weight on root causes and biological mechanisms—the “how” and “why” behind disease. We also look at health through the lens of evolution: humans evolved in environments very different from modern life, so today’s diet, stress, and low activity can clash with our biology. We believe this “mismatch” is a significant reason chronic diseases have become so common. Because health research can be messy — short studies, hard-to-control lifestyles, biased funding, and overreliance on weak observational data — we treat “evidence” carefully. We integrate multiple types of data and artifacts, from human and animal studies of medical science to archeology and genetics, to seek biologically meaningful patterns, not merely statistically significant ones.

How do we ensure cutting-edge science?

Mainstream scientific establishment is systemically biased and structurally broken. How do we avoid the typical biases of conventional and alternative medicine and individual practitioners?

Medical Research team

Our Medical Research team builds the knowledge base for the Ancestra Health app. The team comprises scientists with backgrounds in molecular biology, biochemistry, nutrition, and medical science. All members have a deep understanding of the biological mechanisms underlying healthy and pathological states, as well as their interactions. They think holistically because symptoms and diseases are often manifestations of underlying pathologies that share common causes.

When we expand our knowledge base to include a new disease, the research team reviews relevant papers, including recent and older scientific literature. Our team is up to date on the latest scientific findings and theories. Knowledge sharing is a key component of their workflow.

Critical thinking

Because the medical literature is generally low in quality and biased, our team members must be strong critical thinkers. Being able to synthesize knowledge from a vast number of biased, variable-quality papers is vital. We use LLMs trained to mitigate common biases to systematically search the scientific literature and identify and summarize the most relevant papers on the subject.

Assessing the strength of evidence

"All models are wrong, but some are helpful." [ * ] This phrase is especially useful in biology, where the deeper you go into the details and the broader context you choose, the more anomalies you find. We should be aware that all models provide acceptable approximations only within a given context. When we compare theories or develop our own, we collect papers on the subject and assess the evidence they provide. Often, even clinical trials (outcome studies) don't prove convincing because of the biases we find in them. Animal studies are necessary because they typically provide greater mechanistic detail than human studies. However, the animals used in the study may respond differently from humans. Observational (epidemiologic) studies alone provide less evidence than well-designed clinical trials. Still, if there are no obvious biases in the studies and the effect size is large, they can support the case. [ * ] Anecdotal evidence shouldn't be neglected either. If a large number of people's stories overlap significantly, we should not dismiss them. Our research team considers and synthesizes evidence from multiple sources and, when necessary, develops new mechanistic models based on it.

Our Technology

To translate research into practical guidance, the Ancestra Health app uses an in-house built, special type of AI, a Bayesian Network. We also use other AI tools to scan and summarize large volumes of scientific information while identifying common biases in the scientific literature.

Bayesian Network

The Ancestra Health app's knowledge base is built on a Bayesian Network — a structured, probabilistic graphical model. It represents hundreds of linked health variables, mapping their causal relationships through a directed acyclic graph and expressing their conditional dependencies as probabilities (e.g., how strongly a sign might predict a specific condition). Our Bayesian network is trained on thousands of so-called requirements, extracted from or inferred from tens of thousands of scientific papers.

The requirements used for training have this structure: the conditional probability of a state of variable A, given the observation of a state of variable B, is X%: P(A = a | B = b) = X% For example, the probability of insulin resistance given skin tags is 96%: P(insulin resistance = present | skin tags = present) = 96%

Extraction of information using LLMs

We use Large Language Models (LLMs) to retrieve information from scientific papers and other online sources. The LLMs are trained to detect common biases in these sources, increasing the effectiveness of our researchers.