Perle

$0.0958
-3.7%(24h)Today
-3.7%
30 Days
-15.0%
Price Change (24h)
-$0.003672

Today
-3.7%
30 Days
-15.0%
Price Change (24h)
-$0.003672
The live price of Perle is $0.0958 per PRL/USD, -3.69% in the last 24 hours. It has a market cap of $22.29M and $991.67K in trading volume over the same period. Prices are averaged across every exchange we track and update continuously. PRL has a circulating supply of 175M.
Current price
$0.095824h high
$0.099824h low
$0.094524h
-3.69%7d
-14.59%30d change
-14.95%Rank
#341
By market cap
Market Cap
$22.29M
24h Volume
$991.67K
Across 6 exchanges
FDV
$95.83M
If all tokens were issued
Circulating Supply
175M PRL
17.5% of max supply issued
Total Supply
1B PRL
Max Supply
1B PRL
Listed on
6 exchanges
Perle is available on 6 exchanges. Prices shown are live.
| Exchange | Pair | Volume | Trade |
|---|---|---|---|
| Bitget | PRLUSDT | $1.35M | Buy PRL on Bitget |
| LBank | prl_usdt | $796.33K | Buy PRL on LBank |
| Coinbase | PRL-USD | $279.56K | Buy PRL on Coinbase |
| MEXC | PRLUSDT | $124.12K | Buy PRL on MEXC |
| Gate | PRL_USDT | $101.49K | Buy PRL on Gate |
| KuCoin | PRL-USDT | $86.70K | Buy PRL on KuCoin |
Price: $0.0958 — Price Source: 6 exchanges
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Perle is a platform built on the Solana blockchain that combines web3 technology with artificial intelligence. It aims to connect verified experts, including professionals and researchers, with enterprises and research teams seeking to improve their AI models. The platform focuses on tasks such as data annotation and validation, where contributors can earn rewards and develop an on-chain reputation based on the quality of their work.
The project addresses several challenges in the AI development space. One issue is the bottleneck created by the ongoing need for large volumes of high-quality annotated data, which existing pipelines often struggle to scale. Another concern is the reliance on general labor pools that may lack specialized domain knowledge, leading to lower-quality feedback and reduced AI accuracy. Additionally, the platform seeks to address a lack of verifiability in AI training processes by providing clear audit trails for contributor performance and quality.