Trading activity:
Reported 24-hour volume equals 0.31% of market capitalization. The local markets snapshot lists DigiFinex among venues with observed trading activity.
YearBull indicators:
YearBull Rank #865. Bull Score 87/100.
YB Market Risk Low. This relative market-volatility label is not an investment-safety assessment.
Cycle Early. Observed price change: 24h -5.47% · 7d -13.06% · 30d 40.05%.
Values are descriptive and should be read together rather than as a price forecast.
Read the YearBull methodology.
Snapshot date: 2026-10-27.Data history: 90 days available in the latest 90-day window.
Methodology responsibility:
YearBull’s analytical methodology and presentation rules are developed and maintained by
Alan Zelvin, Founder & Lead Crypto Researcher.
This note identifies responsibility for the methodology; it does not attribute authorship of this data snapshot.
What is KardiaChain (KAI)?
Source description
“KAI Network is a next-generation blockchain designed to turn AI breakthroughs into on-chain value. It enables developers, content creators, prompt engineers, and researchers to earn $KAI by discovering novel techniques—such as prompts, workflows, model exploits, or parameter optimizations—and minting them as digital assets. These discoveries become NFTs (called DCVRs) that generate royalties and can be licensed or traded across the ecosystem. Whether you’re building tools, running agents, or just experimenting with AI, KAI lets you capture the value of your creativity—directly, transparently, and on-chain.”
This source-supplied description may contain old, promotional, or unverified claims and is not YearBull editorial analysis.
Recorded networks: Kardiachain, Binance Smart Chain, Arbitrum One
Official links and contract records appear in Key Facts. Project details can change, so verify current information with the project.
KardiaChain (KAI) FAQ
How does the project turn AI discoveries into digital assets?
The project states that developers, content creators, prompt engineers, and researchers can identify AI techniques—such as prompts, workflows, model exploits, or parameter optimizations—and mint those discoveries as digital assets. These assets are called DCVRs, described as NFTs that can represent the underlying creative or technical contribution within the project’s ecosystem.
What are DCVRs, and how can they generate value for their creators?
DCVRs are NFTs that the project describes as tokenized AI discoveries. According to the project, their creators may earn royalties when the discoveries are used, licensed, or traded across the ecosystem. This positions DCVRs as a way to record and potentially monetize techniques such as prompts, workflows, model exploits, and parameter optimizations on-chain.
Who can participate in the project’s AI discovery economy?
The project presents its ecosystem as open to several groups, including developers, content creators, prompt engineers, and researchers. It also mentions people who build tools, run agents, or experiment with AI. These participants are described as being able to discover techniques, mint them as DCVRs, and earn $KAI through the project’s proposed on-chain model.
What kinds of AI techniques can be minted as DCVRs?
The project specifically identifies prompts, workflows, model exploits, and parameter optimizations as examples of discoveries that can become DCVRs. This suggests the proposed asset model is intended to cover both creative inputs and technical improvements connected with AI systems. The project describes these discoveries as digital assets that may be licensed or traded across its ecosystem.
How does the project describe licensing and trading for AI discoveries?
The project states that DCVRs can be licensed or traded across its ecosystem, allowing the represented AI discoveries to circulate beyond their original creation. It also describes these NFTs as capable of generating royalties. Together, these claims present a model in which creators can retain an on-chain representation of their work while enabling other participants to access or exchange it.
KardiaChain metric comparison
This comparison is a stored snapshot generated 2026-10-23 06:30 UTC from 292 daily observations available from 2025-12-30 through 2026-10-23. It is separate from the latest analytical cards above. Percentiles compare the snapshot value with that day's analytical universe; a higher percentile means a larger observed value, not necessarily a better investment characteristic.
Metric
Snapshot
30d before
90d before
Change vs 30d
Universe percentile
Price
$0.00036754
$0.00019896
$0.00017586
+84.7%
n/a
Market cap
$1.76M
$950.0K
$839.7K
+84.7%
P71.2
YearBull Rank
#622
#2,979
#5,482
Improved 2,357
P92.7
Bull Score
95/100
49/100
17/100
+46.0 pts
P100.0
Turnover
0.30%
0.64%
0.68%
-0.3 pts
P42.9
YB Market Risk
High
Low
Low
Low → High
n/a
Cycle
Early
Early
Early
Unchanged
n/a
Median absolute daily movement 4.21%; distance from the highest local daily price -64.0%; circulating supply change 0.0%. These measurements are descriptive and do not predict future direction.
YearBull Rank update
Newest YearBull Rank value for kardiachain: #865.
Rank timeline (last 365 days)
Rank change (daily snapshots).
Reading rule: smaller rank numbers are better.
7d window (2026-10-20): #748 → #865 (down by 117).
30d window (2026-09-27): #1102 → #865 (up by 237).
YearBull Rank is an internal ordering on YearBull that positions a coin relative to the rest of the tracked universe. Lower rank numbers correspond to stronger relative placement.
Market access: If rank holds gains, the footprint is likely supporting the move.
Risk view: If the last month is chaotic, widen the lookback before concluding.
Cycle angle: If the 7d is weak but 30d is strong, it can be a pullback in an up-phase.
Turnover context: If the line flatlines, the coin may be moving with its liquidity peers.
Editorial note:
This analysis was prepared by the YearBull research team under the direction of
Alan Zelvin,
Founder and Lead Crypto Researcher.
The assessment follows YearBull’s internal research methodology and editorial standards.
Methodology ·
Editorial Policy
Related Research
Continue with contextually connected profiles and category data:
Gram analysis - nearby market scale in the current dataset.
Tharwa analysis - nearby market scale in the current dataset.
Stored venue snapshot. Markets last checked: 2026-10-20. Next refresh window: around 2027-01-18. Venue listings and volumes are stored snapshots, not live quotes.
