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Silencio

Silencio (SLC) — 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).

Silencio — a DePIN / AI/Data project: a crowdsourced network for collecting real voice and audio data (speech + ambient sound) for the purpose of training models (TTS, speech, robotics, context‑aware AI). Participants contribute data via the app/web portal, and the SLC token is used in the network economy (rewards, access to datasets, staking, buyback&burn — per the whitepaper). Onchain SSOT of the token: Base (ERC‑20); the whitepaper also describes the choice of peaq as the L1 for DePIN device identifiers and onchain consents.

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: a network that pays people for collecting voice/audio data for AI training.
  • Who it’s for: participant‑contributors (data collection) and companies/researchers (dataset purchase).
  • Why onchain/token: the SLC token is used for rewards/staking and as the currency of access to datasets; onchain is needed for the verifiability of consents and the transparency of economic flows (claimed).
  • What you can actually touch (Tier‑1): the “Train AI” participation portal — https://www.silencio.network/train-ai

How it works (very briefly)

  1. The contributor does: registers and records voice/audio (TTS scripts, commands, conversational speech, ambient sounds) via the app or the web portal.
  2. The network does: collects data + metadata (language, accent, recording conditions), validates quality (AI checks + human review) and forms datasets for ML/AI.
  3. The contributor receives: USD rewards/internal coins and participation in the raffle mechanics; the SLC token figures in the network economy.
  4. Where the blockchain is: the SLC token is an ERC‑20 on the Base network (contract on BaseScan). The whitepaper describes onchain consents (consent) and onchain tracking of activity/staking/burns (via Dune).
  5. Token role: access to datasets (data marketplace), rewards/raffle, staking and buyback&burn structures (per the whitepaper).

Quick links: AI thesis fit

Project passport

Field Value
Project name Silencio
Also known as (aliases) SLC, Silencio Network
Logo (logo) ../assets/223_silencio_logo.svg
Token symbol (ticker) SLC
Category AI/Data | DePIN | Infra
Primary chain (chain) Base
Type EVM
Network status mainnet
Consensus (consensus) L2 (Optimistic Rollup, Base on Ethereum)
Launch date (launch_date) 2022 (MVP stage); in the whitepaper: launch on peaq 2024‑09‑12 (migration of activity onchain)
Project status (status) active
Token (standard) ERC-20
Token contract 0x6Bd83ABC39391Af1E24826E90237C4BD3468b5D2
Token model (token_type) utility (governance - future phase)
Max supply 2,712,729,921.177779 SLC (Max Total Supply on BaseScan)
Issuance capped (max total supply listed) + burn structures (buybacks/burns — per the whitepaper)
Tokenomics (tokenomics, brief) $SLC is used for access to datasets and rewards; 75% of revenue — to buybacks (half is burned), 25% — treasury (per the whitepaper)
Governance progressive decentralization (governance — future phase)
Admins/keys —
Audit —
Bug bounty —
Team (team) —
Funding (investors/funding) — (the website has “Trusted by leading investors…”, but the list/terms are not verified)
AI component (ai_component) voice + ambient audio datasets for AI
Use‑case (use_case) collection + sale of audio datasets for AI
DecAI Fit — AI thesis fit 6/6 — high alignment with the Decentralized AI thesis
DecAI Fit component summary 3 / 3 / 2 / 3
Last verified 2026-02-11

General project overview

Silencio positions itself as “The World’s Ears for AI & Robotics”: a network that collects real voice and ambient audio data from a large number of participants around the world and turns them into datasets for training speech/audio models (including use‑cases for robots and autonomous agents).

Judging by the whitepaper, the product is built around private, “privacy‑first” onboarding, explicit consents to data use and quality control mechanisms (automatic AI checks + human validation). Separately, the project has a B2B “data products” storefront with the categories Audio/ML/LLM/Speech datasets.

Features/modules

  • Silencio App / web portal: collection of voice and audio data, participation in rewards.
  • Silencio Voice AI: a platform for collecting voice data for TTS/ASR training (the architecture is described in the whitepaper).
  • Data marketplace / storefront: a B2B storefront of data products (speech/audio/noise datasets).
  • On‑chain consent and transparency: recording of consents and publication of network metrics/economics onchain (per the whitepaper).

Typical scenarios

  • Scenario 1 (contributor): a user records voice/audio tasks → receives USD/coins and participation in raffle rewards.
  • Scenario 2 (data buyer): a company buys access to a dataset (noise/speech/ambient) → uses it for training/evaluating models.
  • Scenario 3 (Web3 participant): an SLC holder stakes the token (in‑app or onchain) → receives yield/bonuses (per the whitepaper).

Getting started (wallet/network)

  • What you need: the Silencio app (iOS/Android) and/or registration via the “Train AI” web portal.
  • Network/chain: for the token — Base; in the whitepaper, the consent/DePIN layer is described on peaq.
  • Fees: network fees for onchain operations with SLC (Base) and/or for consent/staking actions (per the whitepaper).

Architecture/tech (non‑AI)

  • Components: mobile apps (Swift/Kotlin), in‑browser Silencio Voice AI, a backend data layer, the SLC token (ERC‑20, Base).
  • Participant roles: contributors, validators (AI + community + internal team), dataset buyers, token holders/stakers.
  • Where logic runs: audio collection/validation — offchain; consent log (device ID, permissions) — onchain (peaq — per the whitepaper); token economy/SLC accounting — onchain (Base token SSOT).
  • Dependencies: Particle Network (AA wallet/sensor account), peaq network, the Base/Ethereum ecosystem, analytics via Dune (per the whitepaper).

Token/economics (expanded, no forecasts)

  • Token utility: $SLC is claimed as the “native currency” for access to datasets (data marketplace), as well as for rewards/raffle, staking and premium features (per the whitepaper).
  • Protocol necessity drivers: the protocol is built around a reward economy and settlement in $SLC; without the token, incentivization and the access/settlement model break down (per the whitepaper’s claims).
  • Who pays fees: users/buyers/stakers during onchain operations.
  • Where fees go: part of the revenue goes to buybacks/burns and the treasury (per the whitepaper).
  • Issuance/burn: the whitepaper describes buybacks and burns; BaseScan shows the token’s max total supply.
  • Security/incentive mechanisms: raffle mechanics of reward distribution and dual staking (in‑app + on‑chain) — per the whitepaper.
  • Distribution and token issuance schedule: see the Token Distribution / Investors / Core contributors sections in the whitepaper (not detailed in this card).
  • Treasury: in the whitepaper: 25% of the revenue flow to the treasury (in the context of buybacks/burns).

Governance

  • Who makes decisions: progressive decentralization (governance — future phase; a detailed process is not described as a working mechanism).
  • How protocol parameters change: —.
  • Where decisions are discussed/made: public channels (X/Discord/Telegram) + the whitepaper.
  • Keys/multisig: —.

Security and incidents

  • Audits: — (there is no audit report in links_*).
  • Bug bounty: —.
  • Incidents: —.
  • Threat model: privacy/consent risks, data quality risks (fraud/duplicates), token economy risks (dependence on buyback/burn), onchain migration risks (the whitepaper indicates a phased migration of activity).

Project status and timeline

  • Current status: an active data collection network + the SLC token on Base.
  • Key releases/milestones: 2022 MVP; 2024 growth; 2025 Voice AI; 2026–2030 Silencio Live Audio (per the whitepaper).
  • Roadmap: https://whitepaper.silencio.network/silencio-introduction/our-mission-and-roadmap

Ecosystem/integrations

  • Integrations: iOS/Android app; BaseScan (token); peaq (consent/device IDs — per the whitepaper); Dune dashboard (analytics — per the whitepaper); the storefront of datasets.
  • Partners: the website lists “Trusted by” logos, but legally/contractually this is not verified in this card.
  • Where it’s actually used: sales of data products via the storefront; the data collection network via the app/portal.

AI component of the project

  • What is real AI here? Real: the collection and preparation of datasets for training speech/audio models (TTS, command datasets, ambient sounds), as well as automated AI validation of recording quality (described in the Voice AI architecture).
  • What is truly onchain? The SLC token (ERC‑20, Base). The whitepaper describes onchain consents (consent) and activity tracking (staking/burning/reward issuance) with publication of metrics via Dune.
  • Why is a token needed? For settlement for access to datasets, reward mechanics (raffle/coins→tickets) and staking; also, part of the revenue is directed to buyback&burn (per the whitepaper).
  • Red flags (based on the information found): a mix of several networks (Base for the token, peaq for consent — it is important to double‑check which actions happen where); some metrics/client logos are marketing and require verification by contracts/LOI; access to Dune may require JS.

DecAI Fit component summary

  • DecAI Fit: 6/6 — high alignment with the Decentralized AI thesis
Component Level Score
AI reality high 3
Onchain necessity medium 3
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): There are signs of AI reality per Tier‑1 (see AI_1..AI_4), so the AI_0 flag (intent‑only) does not apply.
  • Evidence: Tier‑1 — see Evidence/links in AI_1..AI_4.
  • Why sufficient / why not (one line): AI_0=1 is admissible only when AI_1..AI_4=0; here ai_score without AI_0 = 3.
  • Conflict note (if any): none.

AI_1 — what the AI does (1/0)

  • AI_1 (decision): 1
  • Claim (one line): the network collects voice/audio recordings with metadata and forms datasets for training speech/TTS and audio AI; quality is validated by AI models and human review.
  • Evidence: Tier‑1 — https://whitepaper.silencio.network/technical-overview/silencio-voice-ai-architecture ; https://www.silencio.network/
  • Why sufficient / why not (one line): the architecture document describes the data flow and validation layers, sufficient to confirm the AI component.
  • Conflict note (if any): none.

AI_2 — demos/examples (1/0)

  • AI_2 (decision): 1
  • Claim (one line): there is a public “Train AI” portal for data collection and a public storefront of B2B data products.
  • Evidence: Tier‑1 — https://www.silencio.network/train-ai ; https://storefront.silencio.network/
  • Why sufficient / why not (one line): the pages show the practical side: how to participate and which datasets are sold/exported.
  • Conflict note (if any): the map explorer map.silencio.store is accessible: https://map.silencio.store/

AI_3 — metrics/quality (1/0)

  • AI_3 (decision): 0
  • Metrics: — (quality metrics of models/datasets as KPIs are not provided in the form of benchmarks).
  • Context (version/date/how measured): —.
  • Evidence: Tier‑1 — https://whitepaper.silencio.network/technical-overview/silencio-voice-ai-architecture
  • Why sufficient / why not (one line): the document describes the process and quality checks, but does not publish measurable quality metrics of the resulting models.
  • Conflict note (if any): none.

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

  • AI_4 (decision): 1
  • Claim (one line): the Voice AI architecture (data flows, metadata, validation, dataset export) and on‑chain consent are described in detail.
  • Evidence: Tier‑1 — https://whitepaper.silencio.network/technical-overview/silencio-voice-ai-architecture ; https://whitepaper.silencio.network/technical-overview/on-chain-consent
  • Why sufficient / why not (one line): this provides a verifiable technical description of the key modules.
  • Conflict note (if any): none.

Onchain necessity

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

  • OC_1 (decision): 1
  • Chain: Base mainnet
  • Addresses/identifiers: ERC‑20 SLC: 0x6Bd83ABC39391Af1E24826E90237C4BD3468b5D2
  • Evidence: Tier‑1 — https://basescan.org/token/0x6Bd83ABC39391Af1E24826E90237C4BD3468b5D2
  • Why sufficient / why not (one line): BaseScan confirms the contract, decimals and max total supply.
  • Conflict note (if any): in the whitepaper, a significant part of the consent/DePIN activity is described on peaq (a separate network), so it is important to distinguish the “chain of the token” from the “chain of network actions”.

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

  • OC_2 (decision): 1
  • What useful logic is onchain: onchain consents (consent), accounting of staking/burning/rewards and publication of auditable network activity (per the whitepaper).
  • Evidence: Tier‑1 — https://whitepaper.silencio.network/technical-overview/on-chain-consent ; https://whitepaper.silencio.network/silencio-introduction/on-chain-activity
  • Why sufficient / why not (one line): specific categories of actions that are recorded onchain and available for audit are described.
  • Conflict note (if any): without contract addresses (other than the token), the assessment is limited to the description.

OC_3 — onchain is critical (1/0)

  • OC_3 (decision): 1
  • “Replace the blockchain” test: without onchain, the verifiable history of consents and the auditable transparency of rewards/burns are lost, which is the key value prop of the DePIN network.
  • Evidence: Tier‑1 — https://whitepaper.silencio.network/technical-overview/why-blockchain-why-peaq ; https://whitepaper.silencio.network/technical-overview/on-chain-consent
  • Why sufficient / why not (one line): the whitepaper explicitly argues that machine IDs + onchain consent are critical for trust in the datasets.
  • Conflict note (if any): the audio collection itself is also possible in a centralized way; criticality relates to “verifiability/ownership”.

Token necessity

TK_0 — no token (1/0)

  • TK_0 (decision): 0
  • Claim (one line): the project has a token, $SLC.
  • Evidence: Tier‑1 — https://basescan.org/token/0x6Bd83ABC39391Af1E24826E90237C4BD3468b5D2 ; https://whitepaper.silencio.network/tokenomics
  • Why sufficient / why not (one line): the existence of the token is confirmed by the explorer and the whitepaper.
  • Conflict note (if any): none.

TK_1..TK_4 (1/0)

TK_1 — payment for the service (1/0)

  • TK_1 (decision): 1
  • Claim (one line): $SLC is claimed as the currency of access to datasets in the data marketplace.
  • Evidence: Tier‑1 — https://whitepaper.silencio.network/tokenomics/utility-of-the-usdslc-token
  • Why sufficient / why not (one line): the utility page directly describes the “Data Marketplace & Access Layer” and the categories of purchasable datasets.
  • Conflict note (if any): it is stated that a FIAT→$SLC conversion “in the background” is possible, which reduces the mandatory nature of the token for a Web2 buyer.

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

  • TK_2 (decision): 1
  • Claim (one line): the reward economy includes raffle distribution and dual staking (in‑app + onchain).
  • Evidence: Tier‑1 — https://whitepaper.silencio.network/tokenomics/utility-of-the-usdslc-token ; https://whitepaper.silencio.network/tokenomics
  • Why sufficient / why not (one line): incentive mechanisms are described at the tokenomics level (without detailed contract parameters in this card).
  • Conflict note (if any): none.

TK_3 — governance (1/0)

  • TK_3 (decision): 0
  • Claim (one line): governance via $SLC is described as a future phase (progressive decentralization).
  • Evidence: Tier‑1 — https://whitepaper.silencio.network/tokenomics/utility-of-the-usdslc-token
  • Why sufficient / why not (one line): this is an intent, not a proven working governance mechanism today.
  • Conflict note (if any): none.

TK_4 — token is replaceable (1/0)

  • TK_4 (decision): 0
  • Claim (one line): for the protocol’s incentive economy and settlement, the token is claimed as a “vital and indispensable” element of the network.
  • Evidence: Tier‑1 — https://whitepaper.silencio.network/tokenomics
  • Why sufficient / why not (one line): the whitepaper directly asserts the irreplaceability of the token for incentives within the network design.
  • Conflict note (if any): for a data buyer, a FIAT→$SLC conversion is possible, which reduces the “rigidity” of the dependence on the token at the UX level.

Evidence (table)

Tier Source What it proves
Tier‑1 https://www.silencio.network/ Positioning: “World’s Ears for AI & Robotics”, the scale of the network and the dataset directions.
Tier‑1 https://www.silencio.network/train-ai The public participation path: recording of voice data and the reward mechanics (USD/coins/raffle).
Tier‑1 https://whitepaper.silencio.network/technical-overview/silencio-voice-ai-architecture Technical description of Voice AI: collection/metadata/quality validation/export of datasets for TTS.
Tier‑1 https://whitepaper.silencio.network/technical-overview/on-chain-consent Onchain consents (consent), sensor wallet (Particle), the list of actions recorded onchain.
Tier‑1 https://whitepaper.silencio.network/tokenomics/utility-of-the-usdslc-token Token utility: data marketplace, staking, raffle, premium features, buyback/burn.
Tier‑1 https://whitepaper.silencio.network/silencio-introduction/on-chain-activity A statement that significant network activity is published onchain + Dune dashboard; a note about the migration after the launch on peaq (09/12/2024).
Tier‑1 https://basescan.org/token/0x6Bd83ABC39391Af1E24826E90237C4BD3468b5D2 Onchain SSOT of the SLC token on the Base network: contract/decimals/max total supply.
Tier‑1 https://storefront.silencio.network/ The existence of a B2B storefront of data products (Audio/ML/LLM/Speech datasets) and dataset examples.

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