The AI-Native Oracle that interprets reality and proves it. Backed by @yzilabs
Joined January 2026
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Cournot is backed by @yzilabs through @EASYResidency Season 3
Cournot interprets real-world events by aggregating multiple sources in real time and applies multi-agent reasoning to reach a judgment. Every source, reasoning step, and conclusion is cryptographically provable onchain, making every judgment verifiable.
Together, we're powering a future where markets, autonomous agents, and onchain applications can securely settle against real-world truth on @BNBCHAIN.
Still building!
PROJECT SPOTLIGHT ON BASE: @CournotProtocol
As Base grows across AI agents, prediction markets, and onchain finance, one question becomes more important:
What should applications trust before they act?
Cournot is an AI-native reasoning oracle built for questions that cannot be answered by a single price or data feed.
Its Intelligence Oracle gives AI agents evidence-backed event probabilities, exact claims, and supporting sources before an outcome is known paying per call on Base. Its Resolution Oracle handles the other side: determining what actually happened once the event resolves.
Cournot’s first major production use case is prediction markets, where it has already:
• Influenced $1.41B+ in notional value
• Completed 23,732 resolutions
• Reached 99.80% accuracy
• Maintained <8 minute latency
Prediction markets are the proving ground. The same infrastructure can extend into trading intelligence, RWA, private credit, DeFi, compliance, and agent-driven finance.
Traditional oracles made offchain data usable onchain.
Cournot is building the layer that helps applications and agents understand what that data actually means.
A correct data feed can still produce the wrong settlement.
Imagine a football market that resolves on regulation time only, but the match is eventually won in extra time. An API may correctly return the final winner and still give the market the wrong answer.
The oracle needs to understand the market’s actual rules, identify the relevant evidence, and map that evidence to the permitted outcome.
That’s where Cournot’s AI layer matters.
It doesn’t just ask: “What did the API say?”
It asks: “Is this the right evidence for the decision this market needs to make?”
For halftime, first-goal, regulation-time, and other structured sports markets, that distinction is what turns raw data into reliable resolution.
We know there will be a lot of amazing builders and founders building in the upcoming BNB Bangkok Hackathon.
As a featured Oracle provider in the @BNBCHAIN Kickstart Program and as a part of @yzilabs family and @EASYResidency alumni, Cournot welcomes the builders and founders with:
🔸 Up to 3 months free access to Cournot's Resolution and Intelligence Oracle access for eligible BNB Chain projects
🔸 AI-native multi-source event resolution
🔸 Probability intelligence with claims + sources
🔸 Priority API / agent integration support
🔸 Support across prediction markets, agents, RWA, DeFi & gaming
🔸 Custom workflows and+ co-marketing for selected teams
Don't miss it out. Build and build!
Big ideas meet the Big Mango 🥭
BNB Bangkok brings builders and founders together Nov 25-27 for cash prizes, investor pitches, and fast-track interviews for @EASYResidency
Two tracks. Here’s where you fit 🧵👇
What if verifying AI-generated outcomes didn't require trusting the AI, exposing private data, or running the same computation across multiple nodes?
That's the foundation of Cournot's verifiable trust architecture.
Traditional oracle systems rely on multiple nodes independently executing computations to reach consensus. While effective, this introduces significant computational overhead, especially for AI-powered inference.
Cournot takes a different approach, combining three complementary technologies:
🔸 zkTLS | Verifiable Evidence
Cryptographically proves that data originates from an authentic source, without exposing the underlying sensitive information.
🔸 opML | Verifiable Reasoning
Enables AI inference to be challenged and verified through fraud proofs, rather than requiring every node to repeat the same expensive computation.
🔸 TEE | Confidential Computing
Protects sensitive inputs and AI reasoning within hardware-secured environments, enabling private data to be evaluated without exposing it to infrastructure operators.
Together, these technologies establish Cournot's One-Honest-Node Verification Model, where a single honest participant can challenge incorrect inference without requiring redundant execution across the network.
The result is a fundamentally different trust model:
Verified inputs → Reproducible reasoning → Deterministic computation → Verifiable on-chain outcomes.
The application of Cournot's architecture extends beyond prediction market resolution. Its underlying architecture can serve as a verifiable intelligence layer for financial systems, real-world assets, insurance, compliance, and autonomous AI agents.
Processed with less redundant computation, stronger privacy guarantees, and verifiable intelligence.
Because the future of decentralized intelligence shouldn't depend on trusting AI. It should be built on the ability to verify it.
Explore Cournot's architecture: docs.cournot.ai/architecture…
A new one from @42space relevant to Jensen Huang and we are monitoring it 👀
42.space/event/0x039d08d7486…
When in Bangkok, don't miss out Muay Thai sessions with fellow builders 👀 💪 🇹🇭 @yzilabs @EASYResidency maybe we can do 'EASY' Hyrox session next?
Miami came back, forced overtime, and finished the job against the Clippers.
Then Cournot went to work.
Within minutes of the result becoming available, Cournot’s Resolution Oracle collected evidence from multiple agents, checked the source and market rules, ran the outcome through its reasoning workflow, and returned a VERY HIGH confidence verdict: Miami Heat.
The full reasoning trail was then published with evidence, verdict, reasoning, and Proof of Reasoning commitments for auditability.
This is what real-world resolution should look like:
Event happens → evidence is gathered → evidence is verified → rules are interpreted → outcome is resolved.
Sports may look straightforward, but at scale, thousands of markets still need to be resolved quickly, consistently, and with evidence anyone can audit.
That’s what Cournot is building for.
Strong and based 👀 build and build!
In two weeks, @CournotProtocol’s Intelligence Oracle call's volume has doubled.
• 20K+ installs
• 14.4K+ active wallets
• 555K+ calls
What’s even more interesting: roughly 40% of this activity is coming from the @base ecosystem, showing that demand is expanding beyond a single chain or user base. dune.com/cournot/cournotorac…
As agents become more capable economic actors, they’ll need reliable intelligence before they act. Cournot is building for that layer.
Hands down we'll keep on building.
The bottleneck for AI-native finance may not be intelligence itself (when to act, when to not act). It may be whether the evidence behind that intelligence can be trusted.
As more onchain applications rely on real-world events, the oracle problem gets harder. Prices and rates are structured; regulatory decisions, product launches, credit conditions, protocol milestones, and other real-world outcomes often depend on evidence spread across websites, APIs, filings, and private records.
For Cournot, that means the trust stack has to start before reasoning begins. A stronger architecture looks like:
Verified Evidence → Cournot Reasoning → Verifiable Judgment
If the source can be authenticated first, Cournot can then evaluate whether the evidence satisfies a defined condition and return an auditable outcome.
That matters for prediction markets, where resolution may depend on proving that a statement really came from the designated source.
It matters even more for private credit, RWA, insurance, and compliance, where the application may only need to verify that a condition is true without exposing the underlying data.
A lender may not need to see a borrower’s exact credit score. It may only need a verifiable answer to: Does the borrower meet the required threshold?
The same principle can apply to liquidity, income, covenants, asset eligibility, or other financial conditions.
For Cournot, the task is clear: strengthen the evidence layer, then reason over that evidence in a way applications and agents can inspect and trust.
That is what turns an AI oracle into infrastructure for verifiable financial intelligence. Let's build together.
Permissionless markets don’t just need permissionless creation. They need scalable resolution.
As anyone can create markets around increasingly niche real-world events, the hard part shifts from listing the question to reliably answering it.
That’s where Cournot comes in: interpreting evidence, comparing sources, and turning complex outcomes into auditable judgments.
Read more on how Cournot can help you to scale permissionless markets: medium.com/@CournotProtocol/…
We’ve been building Cournot to enable exactly what @ellazhang516 described. In an agentic economy, agents won’t just need wallets and payment rails. they’ll need reliable intelligence before they act on behalf of humans.
Through Cournot Intelligence Skills, agents can evaluate real-world events, understand the exact claim, assess probability, and verify the supporting evidence before making a decision.
That means better decisions for humans, and more accountable execution for agents. Payments let agents transact. Intelligence helps them know when, why, and whether they should.
xitter.twitterapi.xyz/CournotProtocol/status…
💡 At @HongKongFinTech Week, @ellazhang516 sat down with @medhy_souidi on where YZi Labs is looking as AI, biotech, and blockchain converge. A few samples:
→ AI × biotech
Longevity is one. Research labs are reversing aging where it used to look inevitable. AI is compressing the research cycle: models, biomarkers, treatments, toward care that fits the individual.
→ Agentic economy
Agents that work have to pay -- fragmented, constant, peer-to-peer. Traditional finance was built for humans and intermediaries. Blockchain clears that 24/7, without a middleman on every hop.
→ Where the three meet
Medical and biologic data sit isolated across hospitals and labs—hard to use for training, hard to share safely. Cryptography puts it in a vault: the model gets insight, the person stays private.
We look for product retention, a durable moat, and a founder the problem won’t let go of.
On top of investing, we’re building an AI-native firm—research, sourcing, diligence, portfolio support—to keep upgrading founder support.
$1.4B Notional Value influenced, with 99.71% accuracy, under 8 minutes.
We keep building cournot.ai/ 💛
We keep building @yzilabs 💛
This incident will only make the crypto industry stronger and more secure than ever. Keep on building @cz_binance @heyibinance and @binance team!
