Official: challengetoken.io Total supply: 600M fixed cap Contract: TBA We never DM first

How CHLG works

CHLG turns real-life challenges into on-chain rewards. Users complete tasks, upload proof, AI verifies authenticity, and CHLG tokens are paid out instantly – all inside a TikTok-native experience.

From challenge to crypto in three steps

Open the app, pick a challenge, record your proof, and let CHLG’s AI engine verify that you really did it. If it’s legit, CHLG tokens land directly in your wallet.

1 · Pick challenge
2 · Record & upload
3 · AI verifies & you earn CHLG

Built for TikTok & Instagram culture

CHLG doesn’t try to change user behavior – it rides the wave of existing trends: daily gym clips, lifestyle vlogs, “day in the life” content, micro-challenges, and viral trends.

Vertical video first UGC native Instant feedback

For users, creators and brands

Users earn CHLG for completing tasks, creators design and host challenge tracks, and brands sponsor campaigns that reward real engagement instead of fake metrics.

Step 1
Open app & choose a challenge

Users see a dynamic feed of challenges based on:

  • Daily streak challenges (e.g. “10k steps”, “morning cold shower”).
  • Community challenges launched by creators.
  • Sponsored challenges funded by brands.
Step 2
Record proof & submit

User records a short video or photo sequence directly in the app or uploads from camera roll (with restrictions).

  • Front camera & face visible when required.
  • Template overlays guide framing and duration.
  • Basic checks run on-device before upload (length, quality, etc.).
Step 3
AI verification & payout

Once uploaded, CHLG’s verification engine scores the attempt and decides:

  • Approved: CHLG tokens are sent to the user’s in-app wallet.
  • Flagged: manual review or partial reward.
  • Rejected: no payout, with feedback on what went wrong.

AI anti-bot verification

At the core of CHLG is a multi-layer AI engine designed to reject deepfakes, loops, stolen content and automation – while keeping the experience fast for real users.

What the AI checks

  • Face & identity consistency: matches user face with their profile and previous challenges.
  • Motion & body tracking: verifies that required movements actually happen (e.g. real push-ups, real running).
  • Frame-level analysis: detects cuts, jumps, overlays, and other signs of editing.
  • Device & metadata: analyzes EXIF, timestamps, geodata (where allowed) and device fingerprints.
  • Behavioral patterns: looks at attempt timing, repetition patterns and win-rates per user.
Computer vision Anti-deepfake Bot detection

Verification outcomes

Each attempt receives a trust score and outcome:

Score band Outcome Example
High Instant auto-approve Clear face, smooth motion, consistent metadata.
Medium Conditional approve Some noise; reward reduced or streak protected but no bonus.
Low Reject / manual review Signs of looping, editing or reused content.

Over time, high-reputation users pass faster, while accounts with suspicious history face stricter checks.

Earning CHLG & progression

CHLG is not just a one-off payout system – it’s a progression loop with streaks, levels and multipliers that reward consistency.

How users earn CHLG

  • Daily challenges: fixed base reward per verified completion.
  • Streak bonuses: increasing multipliers for long streaks (7, 14, 30+ days).
  • Difficulty tiers: higher payouts for more demanding tasks.
  • Sponsored boosts: brands can temporarily boost payouts on their challenges.
  • Creator tracks: completing full challenge series curated by creators.

Levels, XP & staking

  • XP system: each completed challenge adds XP on top of token rewards.
  • Account levels: unlocks access to higher value challenges and early brand drops.
  • Staking CHLG: lock CHLG to unlock:
    • · priority in high-demand challenges,
    • · extra multipliers for long streaks,
    • · access to “whale-free” pools capped per user.
Play-to-progress Anti-whale design

Creators and brand challenges

CHLG gives creators and brands a programmable way to launch their own challenge tracks with on-chain rewards.

For creators

  • Design multi-day challenge series (e.g. “21 days of morning workouts”).
  • Set difficulty, rules and optional locations or props.
  • Receive a share of CHLG emissions for high-completion tracks.
  • Earn from sponsored integrations inside their challenges.

For brands

  • Launch sponsored challenges (e.g. “try our product”, “visit our location”).
  • Fund CHLG rewards that go directly to users, not ad platforms.
  • Access aggregated, privacy-safe stats about completions and engagement.
  • Run regional campaigns with geofenced challenges.

Abuse prevention & safety

Because CHLG connects real actions with real value, rules and safeguards are built into the product from day one.

Abuse & fraud prevention

  • Hard limits on how many high-reward challenges can be completed per day.
  • Rate limits per device, IP range and account cluster.
  • Shadow-banning for bot-like behavior and ring-farming patterns.
  • Blacklist for devices/accounts involved in confirmed abuse.

User safety

  • Content policies defining which challenges are allowed (no self-harm, illegal acts, etc.).
  • Age-gating for certain challenge categories.
  • Clear disclaimers on difficulty/risk level before participation.
  • Dedicated reporting tools for harmful or abusive challenges.

Key questions investors ask

A quick overview of the most common product questions around CHLG’s mechanics.

Is CHLG a fitness app or a social app?

Both. CHLG sits at the intersection of fitness, lifestyle and social – using short-form video as the main interface, like TikTok, but with crypto rewards.

Do users need crypto knowledge to participate?

No. Users sign up with phone / email / social login; CHLG wallet is abstracted behind the scenes. Power users can later connect their own wallets and withdraw.

How do you stop people from just uploading old videos?

AI checks timestamps, metadata, visual context and behavioral history. For sensitive challenges, the app can require real-time capture only.

What happens if AI makes a mistake?

Edge cases go into manual review queues, and users can appeal rejections. Models are retrained continuously using this feedback loop.