LONGEV · LONGEVITY AI BY FUSION AI
A longevity adviser that says when it does not know — and traces everything else to the NIH, the FDA, or a paper you can look up.
Confidence isn’t evidence.
An answer should let you tell the difference.
The model drafts from memory with the library hidden. That draft is never shown; it becomes the search query.
THE PROBLEM
General AI answers health questions confidently, and nothing in the answer shows which parts rest on evidence. In longevity — where a mouse study can sell a supplement to a sixty-year-old — that gap compounds for decades.
THE “WRONG OUT LOUD” METHOD
A private first pass. A plan. Three specialists that challenge the initial guess. Then one answer, with the evidence attached.
Original question from a saved case. The agent workflow is illustrated, not a recorded execution.
A 65-year-old woman has been following an intensive longevity program for two years. Her commercial biological-age score has improved by two years. She is initially pleased, but then realizes that her VO₂max has fallen, her grip strength is weaker, her sleep has deteriorated, and she now struggles with stairs that were previously easy. She asks: “My clinic says the program is working because my biological age went down. But I don't feel healthier. Which should I believe?”
Sensitive health context detected. No names, contact details or patient IDs appear in this case. External checks receive topic-only queries; unresolved privacy checks stop the request.
Biological-age scores and physical function in older adults
Running together. Viewing one report at a time.
Retrieve longevity records. Check the initial guess against the library.
The saved answer references fitness, functional testing and biological-age assessment. These records give the checks a starting point; the IDs alone do not prove a claim.
Check the guess against public sources. This case routes to NIH/NIA.
NIA links physical function to everyday tasks and independence. That supports checking the reported decline. No drug is named, so no FDA drug conclusion is drawn.
Look for new findings that support or challenge the initial guess.
A 2026 chemotherapy trial found age-marker changes were not strongly associated with physical-function changes. It supports possible divergence, within that specific population.
The original question, orchestrator brief and all three cited summaries go to the Composer. Conflicts and missing support stay attached.
A better biological-age score does not settle whether your health has improved. Your reported changes in strength, fitness, sleep and stair climbing deserve clinical review. The sources help explain why the signals may differ; they cannot tell us what caused your decline.
The proposed workflow checks for sensitive information before routing. Unresolved privacy checks stop the request.
Connected checks use topic-only queries. An offline deployment uses reviewed source snapshots.
We can help your business run its own LLM and knowledge base on infrastructure you control.
Expand, limit or customise the longevity library for your business.
Privacy controls and deployment options, tailored to your business. Deployment and data handling are agreed per business. This page does not accept patient information.
NARROW ON PURPOSE
Answer a personal longevity question in a way a clinician would sign off on. That is the hard part of several products — and the limits are part of the argument.
Supplement stacks, biological-age reports, is-this-test-worth-it — to the clinic’s standard.
Plain-English answers; the clinician sees the sourcing panel behind them.
Upper limits, liver-injury grades, interactions — from federal sources, not marketing.
Says what a finding raises, and routes to a clinician.
Longevity and healthspan only. Outside that: no sources, and it says so.
Human-authored, version-controlled. Answers cannot drift.
Live source checks are a separate deployment choice.
The top source is pinned; the tail varies. Documented, not rounded away.
FOR LONGEVITY INDUSTRY INVESTORS
A curated longevity knowledge base, tested evidence retrieval, and example answers open for review.
Clinician review and defined longevity pilots to test the intended benefit.
Knowledge, workflow and local deployment packaged per setting.
Let’s discuss LONGEV.
The questions it can support. The work still ahead.