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Galeon

Galeon — a reference card for a crypto project: what the product does and how verifiable the project’s key claims are; also — alignment with the Decentralized AI thesis (DecAI Fit).

Galeon — an AI/Training | AI/Data | Infra project that claims infrastructure for decentralized AI training on medical data across a network of hospitals (the Blockchain Swarm Learning® concept) + the GALEON token (ERC‑20 on Ethereum; a BEP‑20 on BNB Chain is also listed).

Disclaimer

Important: this page is a reference overview of the project. Within DecAIHub, we separately record a verification based on open sources within the AI thesis (what is real AI/onchain/why a token, and which sources this is based on).
This is not financial advice and not a recommendation to buy/sell/hold any asset. We do not assess price, returns, or entry/exit timing.
Conclusions are based on publicly available materials found as of the verification date. The absence of confirmation does not imply the absence of the technology.

TL;DR (for readers who are not into AI)

  • What it is: DeSci/health‑data infrastructure: a network of participants (hospitals) + rules for value distribution and access to research.
  • Who it’s for: hospitals/researchers/companies that want to train models on medical data while preserving “data sovereignty”.
  • Why onchain/token: the token is used for staking/participation in the DAO fund and for value distribution; the onchain layer records the token and its economy.

How it works (very briefly)

  1. The network participant (hospital/partner) does: joins the network and provides data/participates in joint training (per the BSL concept).
  2. The system does: coordinates the “swarm‑training” process (the data stays with the hospitals; “the algorithms move” between participants) and records actions/value distribution.
  3. Participants receive: the result of the joint training/research and a share of the distributed value (per the claimed model).
  4. Where the blockchain is: the GALEON token is an onchain asset; a blockchain between hospitals is claimed as the layer for training traceability and value distribution.
  5. Token role (if any): voting on research via the DAO fund, staking/participation, and also buyback&burn in the economy (per the whitepaper).

Quick links: AI thesis fit

Project passport

Field Value
Project name Galeon
Also known as (aliases) Galeon Care; GALEON
Logo (logo) ../assets/137_galeon_logo.svg
Token symbol (ticker) GALEON
Category AI/Training | AI/Data | Infra
Primary chain (chain) Ethereum
Type EVM
Network status mainnet
Consensus (consensus) PoS (Ethereum)
Launch date (launch_date) 2022-03-10 (TGE per the tokenomics)
Project status (status) active
Token (standard) ERC-20
Token contract 0x478d742e1c7Ed2A8883Da95E6A5d6b41f2b8545d (Ethereum)
Token model (token_type) utility (staking + governance)
Max supply 4,000,000,000 GALEON (max supply)
Issuance capped supply + buyback & burn (described in the whitepaper)
Tokenomics (tokenomics, brief) max supply 4B; issuance/unlocks by allocations, “remaining tokens released gradually until 2026”
Governance DAO fund (in the whitepaper: voting on research)
Admins/keys —
Audit —
Bug bounty —
Team (team) —
Funding (investors/funding) —
AI component (ai_component) swarm/federated-style model training across hospitals
Use‑case (use_case) training medical models with privacy preservation
DecAI Fit — AI thesis fit 4/6 — medium alignment with the Decentralized AI thesis
DecAI Fit component summary 2 / 1 / 2 / 3
Last verified 2026-02-05

General project overview

In the Tier‑1 whitepaper, Galeon positions itself as infrastructure for Decentralized Science (DeSci) in medicine: hospitals act as “validators”, and AI training is supposed to proceed so that the data stays with each hospital, while the network only coordinates the joint training and the distribution of the value created. The central term in the documentation is Blockchain Swarm Learning® (BSL®).

Important: in the available Tier‑1 documentation this is described primarily as an architectural concept and an economy (how the network should work and how value should be shared), not as a publicly reproducible ML project with demos/metrics.

Features/modules

  • Galeon AI® / BSL® (concept): joint training of models on the distributed data of hospitals; the data “is not put into the blockchain”.
  • DAO fund / voting: token holders and community members can vote on research funding (claimed).
  • Staking: staking of GALEON is mentioned as a participation mechanism.
  • Buyback & Burn: a share of the value under the distribution model is directed to buyback&burn.

Typical scenarios

  • Scenario 1 (researcher/startup): wants to train a model on medical data → requests access to the hospital network → training proceeds in a distributed way (per the BSL claim).
  • Scenario 2 (hospital): participates as a “validator/network participant” → provides data within the scope of consents → receives a share of the value under the distribution rules.
  • Scenario 3 (community/holders): vote on research initiatives via the DAO fund and participate in staking.

Getting started (wallet/network)

  • What you need: an EVM wallet to interact with the GALEON token; access to the whitepaper/official channels to participate in the initiative.
  • Network/chain: the token is on Ethereum (ERC‑20); the tokenomics also lists a BEP‑20 address on BNB Chain.
  • Fees: gas on the chosen network (Ethereum/BNB Chain) for token operations.

Architecture/tech (non‑AI)

  • Components: ERC‑20/BEP‑20 token; an offchain training/data layer; a “blockchain between hospitals” as the traceability layer (without public addresses/an explorer in this card).
  • Participant roles: hospitals (validators/data hosting), research consumers (startups/companies), the community/token holders.
  • Where logic runs: training — offchain at the participants; onchain — the token and its economic mechanics (per the tokenomics).
  • Dependencies: Ethereum/BNB Chain infrastructure (wallets/DEXs/explorers), consent/compliance processes (claimed as a principle).

Token/economics (expanded, no forecasts)

  • Token utility: voting on research (DAO fund), staking, “unlock features in Galeon’s healthcare applications” (per the whitepaper).
  • Protocol necessity drivers: the whitepaper claims that GALEON is used for the operation of the BSL system and for the distribution of the value created.
  • Who pays fees: participants of the token economy for onchain operations; in the value distribution model (example in the whitepaper) the payer is whoever purchases access to the data/training.
  • Where fees/costs go: the whitepaper describes the distribution of value (percentages among patients/holders via the DAO fund, hospitals, the company and buyback&burn).
  • Issuance/burn: max supply 4B; buyback&burn is described in the whitepaper; the tokenomics lists “total supply (Sept 2025) … burned token deducted”.
  • Security/incentive mechanisms: staking is mentioned as a participation mechanism; details of slashing/thresholds are not disclosed on the available pages.
  • Distribution and token issuance schedule: there is an allocation table (staking&rewards/operations/DAO treasury/team/ecosystem growth/public/private/advisors) and a note that the remaining tokens are unlocked until 2026.
  • Treasury: DAO treasury 15% (per the tokenomics); detailed control/multisig is not described in Tier‑1 sources.

Governance

  • Who makes decisions: claimed to be the community/DAO fund (GALEON holders voting on research).
  • How protocol parameters change: — (in this card we record the existence of the governance claim, but without an implementation in contracts).
  • Where decisions are discussed/made: —.
  • Keys/multisig: —.

Security and incidents

  • Audits: —.
  • Bug bounty: —.
  • Incidents: —.
  • Threat model: risks of limited verifiability of BSL onchain artifacts, privacy/compliance risks, risks of centralization of the operational layer.

Project status and timeline

  • Current status: the token exists (ERC‑20 and BEP‑20 addresses in the tokenomics).
  • Key milestones: TGE — 2022‑03‑10 (tokenomics).
  • Roadmap: available in the whitepaper (see links).

Ecosystem/integrations

  • Integrations: Ethereum/BNB Chain (token), DEX purchase (the whitepaper has a Buy section).
  • Partners: —.
  • Where it’s actually used: Tier‑1 data on usage in hospitals/operating ML pipelines is not presented as verifiable artifacts (demos/metrics/repos) → falls under “what to double‑check”.

AI component of the project

  • What is real AI here? Tier‑1 describes joint training of medical models on data distributed across hospitals (BSL®), where the data stays local.
  • What is truly onchain? Tier‑1 confirms the GALEON token (ERC‑20) and its tokenomics; the “blockchain between hospitals” is described conceptually, without public addresses/an explorer in this card.
  • Why is a token needed? voting on research (DAO fund), staking and the distribution of value/incentives, including buyback&burn (per the whitepaper).
  • Red flags (based on the information found): AI‑washing (no demos/metrics), onchain‑washing (verifiable artifacts of the “hospital blockchain” are lacking). Double‑check: whether there are public nodes/an explorer/a BSL repo; whether there are reproducible training cases/metrics.

DecAI Fit component summary

  • DecAI Fit: 4/6 — medium alignment with the Decentralized AI thesis
Component Level Score
AI reality medium 2
Onchain necessity low 1
Token necessity medium 2
Evidence Tier‑1 present 3
Details

AI reality

AI_0 — AI intent (Tier‑1 roadmap) (1/0)

  • AI_0 (decision): 0
  • Claim (one line): AI is claimed as a working part of the concept (not only an “intent”).
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/solution/galeon-ai-r
  • Why sufficient / why not (one line): since there is a description of how the AI is supposed to be trained, this is already more than a “roadmap intent”, so we do not use AI_0.
  • Conflict note (if any): —.

AI_1 — what the AI does (1/0)

  • AI_1 (decision): 1
  • Claim (one line): the network coordinates AI training on the distributed data of hospitals (BSL®), where the data stays local.
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/solution/galeon-ai-r
  • Why sufficient / why not (one line): the AI function (training on medical data) and a high‑level scheme are described.
  • Conflict note (if any): —.

AI_2 — demos/examples (1/0)

  • AI_2 (decision): 0
  • Claim (one line): a public demo/repository/reproducible example of model training was not found in Tier‑1 sources.
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/solution/galeon-ai-r
  • Why sufficient / why not (one line): there is a concept and an explanation, but no artifact that can be reproduced or verified.
  • Conflict note (if any): —.

AI_3 — metrics/quality (1/0)

  • AI_3 (decision): 0
  • Metrics: —.
  • Context (version/date/how measured): —.
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/solution/galeon-ai-r
  • Why sufficient / why not (one line): the available pages contain no quantitative quality/efficiency metrics.
  • Conflict note (if any): —.

AI_4 — tech description/architecture/versions (1/0)

  • AI_4 (decision): 1
  • Claim (one line): the architectural idea of BSL® is described (a comparison of local/centralized/federated/“swarm” training; the data stays with the hospitals; “training traceability” via the blockchain).
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/solution/galeon-ai-r
  • Why sufficient / why not (one line): there is a coherent technical description of the approach, although without code/model versions.
  • Conflict note (if any): —.

Onchain necessity

OC_1 — contracts/addresses/chain (1/0)

  • OC_1 (decision): 1
  • Chain: Ethereum (ERC‑20) / BNB Chain (the BEP‑20 address is listed in the tokenomics)
  • Addresses/identifiers: ERC‑20 0x478d742e1c7Ed2A8883Da95E6A5d6b41f2b8545d; BEP‑20 0x1d0Ac23F03870f768ca005c84cBb6FB82aa884fD
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/usdgaleon-token/tokenomics
  • Why sufficient / why not (one line): there are onchain addresses and confirmation in the tokenomics.
  • Conflict note (if any): —.

OC_2 — onchain beyond just a token (1/0)

  • OC_2 (decision): 0
  • What useful logic is onchain: Tier‑1 sources provide no addresses/contracts of the “BSL hospital blockchain” or other useful contracts besides the token.
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/solution/galeon-ai-r
  • Why sufficient / why not (one line): there is a claim about a “blockchain between hospitals”, but without verifiable onchain artifacts.
  • Conflict note (if any): —.

OC_3 — onchain is critical (1/0)

  • OC_3 (decision): 0
  • “Replace the blockchain” test: the description contains no verifiable test showing that without the blockchain the product “breaks” rather than simply losing some of its properties.
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/solution/galeon-ai-r
  • Why sufficient / why not (one line): evidence of onchain criticality is not provided.
  • Conflict note (if any): —.

Token necessity

TK_0 — no token (1/0)

  • TK_0 (decision): 0
  • Claim (one line): the project has a token, GALEON.
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/usdgaleon-token/tokenomics
  • Why sufficient / why not (one line): the token and the addresses are listed in the tokenomics.
  • Conflict note (if any): —.

TK_1..TK_4 (1/0)

TK_1 — payment for the service (1/0)

  • TK_1 (decision): 0
  • Claim (one line): the available tokenomics/token description pages contain no clear formula of “paying with the token for X” as a mandatory payment.
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/usdgaleon-token/the-usdgaleon-token
  • Why sufficient / why not (one line): “unlock features” is mentioned, but without specifics of the payment layer.
  • Conflict note (if any): —.

TK_2 — incentives/collateral/penalties (1/0)

  • TK_2 (decision): 1
  • Claim (one line): staking and buyback&burn are claimed as elements of the economy; the distribution of value among participants is also described.
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/usdgaleon-token/the-usdgaleon-token
  • Why sufficient / why not (one line): staking and buyback&burn are described as incentive/value distribution mechanisms.
  • Conflict note (if any): —.

TK_3 — governance (1/0)

  • TK_3 (decision): 1
  • Claim (one line): holders/the community vote on research funded by the DAO fund.
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/usdgaleon-token/the-usdgaleon-token
  • Why sufficient / why not (one line): the token description explicitly lists the voting function.
  • Conflict note (if any): —.

TK_4 — token is replaceable (1/0)

  • TK_4 (decision): 0
  • Claim (one line): the token acts as the unit of participation/voting and as part of the value distribution model.
  • Evidence: Tier‑1 — https://whitepaper.galeon.care/whitepaper/usdgaleon-token/the-usdgaleon-token
  • Why sufficient / why not (one line): replacement with a “subscription/USDC” would change the governance/staking model and the claimed distribution economy.
  • Conflict note (if any): —.

Evidence (table)

Tier Source What it proves
Tier‑1 https://whitepaper.galeon.care/whitepaper/solution/galeon-ai-r The BSL® AI concept: distributed training on hospital data + a technical description of the approach
Tier‑1 https://whitepaper.galeon.care/whitepaper/usdgaleon-token/tokenomics TGE, max supply, allocations/vesting, ERC‑20/BEP‑20 addresses
Tier‑1 https://whitepaper.galeon.care/whitepaper/usdgaleon-token/the-usdgaleon-token token roles: voting, staking, unlock features, value distribution
Tier‑2 https://www.galeon.care/ marketing context/positioning (not used to raise flags without Tier‑1)

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