Independent, highest quality health information, now available to enterprises.

Free from pharmaceutical influence, commercial bias, and institutional agenda. Available through API, SDK, and embedded partnership.
Talk to our Team

Proprietary Health Engine. Every source scored & validated. Before it reaches you.

Gary LeBlanc, Chief Operating Officer
"We're designing for perfect dynamic knowledge in a world where everything's changing instantly. The intelligence layer doesn't sit still, it updates as the evidence base updates."
Our Scoring Methodology
130

Evaluation criteria per paper

Every piece of research is scored against 130 independent parameters, not keyword-matched, not summarised by an LLM.
17+

Specialist AI agents

A multi-agent architecture where each agent owns a distinct function: ingestion, scoring, validation, synthesis, retrieval.
18/100

Average research quality score

Across 67 scored papers on peptide therapies. High-volume wellness content doesn't survive the methodology, which is the point.
6.9 pt

Variance in competing systems

The same paper can score 36 on Monday, 43 on Friday in general-purpose models. EverMe scores are deterministic and auditable.
How to integrate

The intelligence meets you
where you build.

MCP Access
In development

As your products adopt agentic AI, EverMe becomes a source your agents can reach natively. Any MCP-compatible assistant connects directly to the EverMe intelligence layer and pulls scored, verified longevity answers in real time. No custom plumbing, no unsourced guesses.

API Access

Available now

Direct programmatic access to the EverMe intelligence layer. Query scored research, retrieve ranked information on any longevity topic, and serve verified answers inside your own product, under your own branding.

SDK / White-label Embed

In development

Embed the EverMe intelligence experience directly into your platform with minimal engineering lift. Your branding, your flows backed by the same scoring methodology that powers the EverMe app.

Strategic Partnership

Available now

For organisations building around longevity at scale. Co-designed integration, shared roadmap access, and the ability to shape how EverMe intelligence surfaces within your ecosystem.

Time to value

Six weeks from intelligence briefing to production

We scope the integration path, agree on data contracts, and have you running against live scored research within the month.
WEEK 1

Intelligence briefing

A focused session with our strategic and technical leads. We understand your use case and data environment. You understand the scoring methodology and what the API delivers.

WEEKS 2-3

Technical scoping

Architecture review. We define the integration path: API, SDK, or embedded, agree on data contracts and topic domain scope. No ambiguity going in.

WEEKS 3-4

Proof of concept

Live integration in your environment with real queries against real scored research, before any long-term commitment.

WEEK 6+

Production rollout

Full deployment with ongoing access to the dynamic knowledge layer. As EverMe's research base grows, your integration improves automatically.

Built with engineering rigor

Two patent-pending systems underpin every answer EverMe produces. Both are deterministic, auditable, and built for enterprise-grade governance.

Research Quality Scoring Framework

A proprietary multi-criteria evaluation system that scores peer-reviewed health research against 130 defined parameters, producing a numeric quality score for each paper. The framework is deterministic, auditable, and reproducible: the same paper receives a consistent score across repeated evaluations, unlike LLM inference which varies run to run. This is the foundation that makes EverMe's knowledge layer governance-based rather than inference-based.
Multi-Agent Intelligence system
Patent Pending
deterministic scoring engine
Patent Pending

Dynamic Knowledge Intelligence Layer

An architecture for continuously updating, re-scoring, and re-ranking health information as new research emerges. Unlike static databases or snapshot-trained models, the dynamic layer ensures intelligence delivered to enterprises reflects the current state of the evidence, not what was available at training cutoff. Designed for perfect dynamic knowledge: always approximating the best available truth, not the last cached version of it.
Why not a general model

EverMe vs general-purpose AI
for health intelligence.

General-purpose models are connection-based, they generate answers from training patterns. EverMe is governance-based, every answer is rooted in scored, citable, auditable research. That distinction compounds at enterprise scale.
Capability
Answers citable to specific papers
Deterministic scores, every run
Knowledge updates as evidence changes
Filters low-quality research by default
Longevity-specific specialist architecture
Protected by filed patents
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EverMe intelligence layer
✓ Every result scored and cited
✓ Auditable, reproducible scoring
✓ Dynamic layer, continuous re-scoring
✓ 130-criteria framework on every paper
✓ 17+ agent system, longevity-native
✓ 2 patents pending
General-purpose LLM
Generated, not cited
Up to 6.9pt variance run-to-run
Dependent on training cycle
No quality gate on input
General purpose, health fine-tuned
No
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