Meta-analysis & systematic review
Pooled RCTs — the most robust evidence we can find in biohacking topics. Examples: creatine monohydrate for strength output, NMN for plasma NAD+ levels.
Every answer backed by 36M+ PubMed papers, A→F evidence tiers and clickable sources. Instead of influencer claims you get what the science actually says — or an honest gap notice when no study exists.
Standard LLMs like ChatGPT, Gemini or Perplexity weren't built for health. They generate plausible-sounding answers — including non-existent studies, wrong dosages and invented authors. On harmless topics it's annoying; on supplements, dosages or interactions it can be dangerous. Studies show ChatGPT hallucinates non-existent source citations in 20-40 % of medical queries. Most "biohacking apps" in the App Store are thin ChatGPT wrappers with a health prompt on top — same hallucination risk, just nicer packaging. Anyone asking "NMN dosage for 45-year-old men" gets a confidently worded recommendation with no real source and no safety disclaimer.
A serious biohacking app meets four requirements: first, every answer with a clickable primary source (PubMed ID, DOI, full-text link); second, evidence rating per study (meta-analysis > RCT > cohort > anecdote); third, explicit gap notification when studies are insufficient — instead of inventing answers; fourth, study freshness, meaning live PubMed access rather than a frozen training date from two years ago. Biohacking AI meets all four: the app searches 36M+ papers live, assigns A→F evidence tiers, flags limited evidence explicitly and provides clickable PubMed links for every claim. The underlying mechanism blocks free generation — the model may only cite from verified sources.
The app offers three main modes. (1) Study chat: ask a question, get an evidence-based answer with 5-10 cited studies in seconds. (2) Deep Research: for complex questions the AI creates a full-text report with 50+ sources, organized by mechanism, study evidence and practical application. (3) Topic worlds: curated entry hubs for sleep, longevity, supplements, performance, recovery and seven additional areas — each with the most robust studies as required reading. The web app runs in any browser without installation; a native iOS app is in beta. All answers stay saved in your account, so you can share, export or roll them into your own stack dossiers later.
Evidence, not hallucination
Evidence-based biohacking means every claim about sleep, supplements, longevity or performance stands or falls with the study it cites. Biohacking AI makes that study trail visible — with clickable PubMed links, transparent evidence tiers and honest labeling where research is still thin. Every biohacker should know whether they're following a meta-analysis or a mouse paper.
Pooled RCTs — the most robust evidence we can find in biohacking topics. Examples: creatine monohydrate for strength output, NMN for plasma NAD+ levels.
Gold standard for single studies. Causal claims are possible, but effect sizes vary widely. Examples: magnesium for cramps, ashwagandha for cortisol-driven stress.
Large population data, but no causality — useful hypothesis generators. Examples: vitamin D levels and mortality, sleep duration and dementia risk.
Plausibility yes, clinical proof no. We label this transparently so no one reads a mouse result as "proven." Examples: peptides like BPC-157, red-light therapy at the cell level.
Those four tiers underpin every answer on the platform — no study is cited without a tier label, and when the evidence is thin the AI says so openly.
For coaches and self-optimizers: AI as a research co-pilot that saves 2 hours of PubMed work per client — without replacing the coach relationship.
Ten curated hubs for sleep, longevity, hormones, supplements and more — each with the most robust studies as required reading.
Ask your first question and see what an answer with a clickable PubMed source looks like. Free, no account needed.