Evidence-graded supplement intelligence, built around your biomarkers
Research dossiers, bloodwork triage, interaction checks, protocol design, and longitudinal tracking in one clinical decision system.
Berberine
Insulin resistance support with lipid and hepatic signal overlap.
Most useful when fasting glucose, HbA1c, triglycerides, and ApoB pattern together as metabolic risk rather than isolated noise.
Pairing glucose-lowering compounds without dose sequencing can exaggerate hypoglycemic risk and blur interpretation on retest.
Research, analysis, and protocol logic in one operating model
The platform is useful because each surface informs the next. Research feeds dossiers. Dossiers inform analysis. Analysis prioritizes what should become a protocol and what should be tracked over time.
Research Library
Searchable compound, drug, and lifestyle evidence by indication, mechanism, biomarker, and risk.
Dossier Detail
Structured clinical briefs with executive summary, evidence table, dosing, interactions, and PGx context.
Bloodwork Analysis
Upload a panel or enter markers manually to surface what matters, why it matters, and what to act on.
Compare
Place compounds side by side across biomarkers, grades, dosing, and clinical tradeoffs.
Interaction Check
Flag compound-compound and compound-drug conflicts before a stack becomes unsafe or contradictory.
Protocol Builder
Turn research into timing-aware stacks built from your goals, labs, and interaction constraints.
Longitudinal Tracking
Follow biomarkers, protocol changes, and retest timing over time so analysis does not end at one panel.
See analysis workflowEvidence, not stack theater
Most supplement products stop at content or commerce. This system ties evidence quality, biomarkers, interactions, and next-step decisions into the same product flow.
Generic supplement platforms
Supplement picks without biomarker context
Cherry-picked claims with no evidence grades
Static stacks that do not adapt to labs
No interaction screening across the full stack
No conflict-of-interest visibility behind recommendations
This evidence-led system
Biomarker-linked reasoning for every recommendation
Explicit A-D evidence grades and methodology visibility
Protocol logic that adapts to markers and clinical goals
Interaction-aware recommendation flow across compounds and drugs
COI-screened dossiers with citations and grading notes
The trust layer is built into the product, not hidden behind marketing copy
Dossiers are built through an explicit research pipeline. Evidence quality, replication, conflicts, biomarker linkage, and interaction logic are visible because they change the recommendation itself.
Grades reflect study quality, replication, and relevance to the actual claim rather than abstract positivity.
Literature sweep
PubMed, meta-analyses, trial registries, and mechanistic papers are reviewed before a dossier is assembled.
Evidence grading
Claims are graded A-D by study quality, replication, population fit, and clinical relevance.
COI screening
Industry funding and author conflicts are flagged so the evidence base is visible, not hidden.
Biomarker mapping
Effects are mapped to concrete markers, directionality, dose context, and expected timing.
PGx review
Relevant genomic modifiers are separated into actionable signals versus exploratory context.
Interaction network
Compound-compound and compound-drug conflicts are screened before protocol logic is exposed.
Clinical structuring
Dosing, safety, evidence, and watch-outs are rewritten into a decision-oriented brief rather than a study dump.
Continuous updates
As evidence shifts, dossiers, triage signals, and recommendations can be updated without rewriting the system.
How the platform gets used
These are the core surfaces that carry the workflow from dossier review to biomarker triage to protocol design.
Dossier excerpt
Executive summary, evidence grade, mechanism overview, safety profile, and interaction flags in one brief.
Prioritize for metabolic risk patterns with glucose, triglyceride, and ApoB overlap rather than isolated curiosity-driven stacking.
Biomarker interpretation
Flagged markers are ranked by urgency, plain-language significance, and likely intervention direction.
Elevated above target with ApoB and triglyceride follow-through suggested before escalation into a full protocol.
Protocol recommendation strip
Primary, adjunct, and optional interventions can be turned into a timing-aware protocol with safety context.
Interaction warning
High-risk pairings are surfaced before they become part of a stack or recommendation path.
Compounds with overlapping glucose-lowering effects should not be stacked blindly when manual triage still lacks companion markers.
Clear tiers for research-first users and full analysis workflows
Start with dossiers and evidence. Upgrade when you need bloodwork triage, personalized recommendations, protocol logic, and ongoing tracking.
Cancel anytime. Use billing management to change plan, payment method, or cancellation status.
“We built this because serious supplement decisions still require too much tab-hopping, too much guesswork, and too little biomarker context.”
The goal is simple: take dense research, expose the evidence quality, connect it to biomarkers, and make the next action obvious enough to use in real decisions.