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Research status: internal. We are not yet publishing raw findings or models. The free guides are how we share useful knowledge right now.
Methodology

How we research

Our pipeline is in silico first: AI reads and structures the evidence, we build causal models of aging, and we simulate interventions in software. Human involvement is limited to non-invasive measurement and strictly reviewed observational studies — never the reverse.

Our research is internal for now: we build before we broadcast, and findings are shared when they are ready — methods and failures included. Until then, the free guides are how we put useful knowledge out without compromising the work.

Pipeline
StageWhat happensHuman involvement
1 · DiscoverAI structures the literature; causal models are drafted and iteratedNone
2 · SimulateInterventions are tested in computational models of aging pathwaysNone
3 · MeasureNon-invasive observational data: wearables, standard blood panels, imagingConsenting participants, low-risk methods
4 · ValidateFindings are checked against independent data and peer reviewNone
5 · ReleaseWhitepapers and guides — shared when ready, including negative resultsNone
Active programs

What we are working on

P01
In progress
Causal mapping of the hallmarks of aging
We are building an explicit causal model of the twelve hallmarks of aging — how they interact, which drive the others, and where intervention has the highest leverage. The model is the backbone of everything else we do.
CAUSAL INFERENCECORE
P02
Operational
AI literature intelligence
A continuously updated knowledge base of aging and longevity research. AI reads, structures, and cross-references peer-reviewed literature so no relevant finding is missed and claims can be traced to sources.
AIKNOWLEDGE GRAPH
P03
Operational
In-silico intervention simulation
The default laboratory. We simulate interventions — molecular, metabolic, and behavioral — in computational models of aging pathways before any human is involved.
SIMULATIONNON-INVASIVE
P04
Validating
Non-invasive biomarker discovery
Identifying the earliest, subclinical signals of disease in routine, low-risk measurements — wearable streams, standard blood panels, and imaging. The goal: intercept disease years earlier.
BIOMARKERSEARLY DETECTION
P05
Reviewing evidence
Reversal mechanism research
Systematic review and modeling of candidate rejuvenation mechanisms — epigenetic reprogramming, senescence clearance, metabolic restoration — with rigorous assessment of the actual evidence for each.
REJUVENATIONEVIDENCE FIRST
P06
Design phase
Prevention-first intervention design
Designing safe, non-invasive, lifestyle-first interventions informed by our causal models. When we reach human studies, they will be observational first, then conservative and fully reviewed.
PREVENTIONETHICS FIRST

We will not trade safety for speed, and we will not trade honesty for attention. The field's reputation depends on organizations like ours refusing to overclaim.

Ethics charter · Souverain Labs
Transparency

What we publish

Our first whitepapers are planned for Q3 2026. They will cover our causal aging model, our literature-intelligence methodology, and — importantly — negative results and open questions.

Until then, research outputs stay internal. The lab log → tracks what the lab is doing, and the free guides → share what we know with the public.

Collab

Work with us

We collaborate with academic labs, clinicians, and data scientists who share our standards. If you work in aging biology, AI for health, or preventive medicine — and you value evidence over hype — get in touch →