AI Agents for Web3: The ChainAware Roadmap

Based on X Space #31 — ChainAware co-founders Martin and Tarmo. March 2025. Watch the full recording on YouTube ↗ · Listen on X ↗

There are 225,000 crypto projects listed on CoinGecko. Most of them face the same two problems that are quietly killing their growth: user acquisition costs so high the unit economics will never work, and fraud rates so severe that users leave before they convert. These are not new problems. Web2 had them too — and solved them with two specific technologies. In X Space #31, ChainAware co-founders Martin and Tarmo lay out exactly how those technologies map to Web3, what ChainAware has built, and how every Web3 business can start benefiting from AI agents today — not in a white paper, not in theory, but in production right now.

The Web2 Parallel: How Two Technologies Changed Everything

To understand where Web3 is going, Martin and Tarmo start where they always do: with the Web2 analogy that most founders haven’t fully internalized yet, because they weren’t there in the early 1990s to live through it.

In the early days of the commercial Internet, e-commerce was struggling with two existential problems. The first was rampant credit card fraud. Consumers were terrified to type their card numbers into a website. Transaction data was being intercepted by network sniffers — malicious tools that harvested credit card details from unencrypted HTTP traffic. This fear directly suppressed transaction volumes. Online businesses were building products people wanted but couldn’t sell at scale because users were afraid to pay. The result: low revenues, skeptical investors, ecosystem-wide stagnation.

The second problem was catastrophically inefficient user acquisition. There was no targeting infrastructure. To drive traffic to a website, companies put ads in newspapers. They rented billboards beside highways with their domain name printed on them. Martin describes it: “There were companies with transparencies beside car roads — buy food at petshop.com. This was the style of marketing at the beginning of the 90s.” Customer acquisition cost was enormous. The first people attracted to these campaigns were technology enthusiasts — maybe 50 million globally — but getting beyond that initial cohort to mainstream adoption was economically impossible at those costs.

Two technologies solved both problems and triggered the exponential growth of Web2. The first was AI-powered transaction monitoring — machine learning models trained to detect fraudulent behavioral patterns before fraud occurred, not after. This crushed credit card fraud rates and restored consumer confidence in online transactions. The second was Google AdWords and the ad tech infrastructure it spawned — the ability to predict user behavior from search history and browsing patterns, and deliver targeted ads that matched user intent. This reduced cost per acquiring a transacting user from hundreds of dollars to $15–$30 in major markets.

The result was not organic adoption or market maturation. It was a specific technology-enabled phase transition. As Tarmo explained: “It wasn’t like some magic crossing the chasm happened. It was technology which enabled it.” Geoffrey Moore’s famous framework for crossing the technology adoption chasm describes what happened but not how. The how was these two technologies removing the two specific blockers that were holding the ecosystem back.

Why Web3 Is Stuck — The Same Two Problems

Web3 in 2025 is in exactly the same structural position Web2 was in the early 1990s. The problems are identical. The missing technologies are the same. The potential of the ecosystem is enormous — and it’s being held back by the same two blockers that held Web2 back a generation ago.

Problem 1: Fraud. According to Tarmo, fraud in Web3 currently represents approximately 15% of Total Value Locked across DeFi — the same percentage as Web2 credit card fraud before predictive fraud detection was introduced. The specific mechanisms are different: rug pulls on PancakeSwap affect approximately 95% of new liquidity pools, wallet fraud rates are extremely high, and the irreversibility of blockchain transactions means the consequences are permanent. But the ecosystem-level effect is identical to 1990s Web2: users get burned, lose confidence, and leave the sector faster than they can learn its benefits. As Martin described: “People are joining the sector, they are going away. They’re joining, they’re going away.” One X user Martin cited had been rug pulled 128 times.

Problem 2: User acquisition cost. The cost of acquiring one genuinely transacting user in DeFi is approximately $1,000–$3,000 — compared to $15–$30 in Web2. This is not a small gap; it’s a factor of 50–100x worse. At these acquisition costs, the unit economics of virtually every Web3 project are structurally negative. Even with zero fraud losses, a DeFi protocol cannot become cash flow positive when it costs thousands of dollars to acquire each transacting user. The projects that do survive either have token treasury subsidies that mask the unit economics, or they get lucky with viral adoption — neither of which is a sustainable growth strategy.

The math is unforgiving. Every business has a unit cost per customer served and a unit revenue per customer. If acquisition cost exceeds customer lifetime value, the business will not survive when its initial capital runs out. This is the quiet economic reality behind the vast majority of Web3 project failures — not bad products, not bad teams, not bad timing. Bad unit economics driven by a missing infrastructure layer.

The Three Real Pain Points of Every Web3 Founder

Martin synthesizes the founder perspective into three specific pain points that emerge from this structural situation. Understanding these precisely matters because the solutions map directly to them.

Pain Point 1: User Acquisition Cost

The most immediately pressing pain point for most founders: how do you acquire users who actually use your product, at a cost that makes the business viable? This is not about generating website traffic — that’s the easy, expensive, and largely useless version of the problem. It’s about acquiring transacting users — people who connect their wallet, engage with the protocol, and generate revenue. The gap between visitors and transacting users in Web3 is enormous, and most marketing spend goes toward generating the former without converting to the latter.

Pain Point 2: Trust and Fraud

Founders are confronted simultaneously by the audit industry (spend heavily on smart contract audits as a trust signal) and by actual fraud risk (bad actors accessing their platform). Both are real concerns. But Martin makes a subtle point that most founders miss: auditing your source code is one dimension of security, but it doesn’t address the question of who you’re letting use your application. “As a founder you want to exclude the fraudsters from your platform. You have to check who do you let to access your platform — is the trust ranking of this gentleman who is using your application high enough, or maybe he has a predictive fraud risk?” Multi-dimensional, multi-layered security requires addressing both the code layer and the user layer.

Pain Point 3: Competitive Advantage in a Copy-Paste Ecosystem

The open-source ethos of DeFi has created an innovation dilemma. When all code is public and copyable, there’s limited incentive to build genuinely novel protocols. Martin observes that most major DeFi categories now have only four or five actual implementations — and most are copies of copies. Uniswap’s function names appear in DEX code on BNB Chain. Compound’s architecture was cloned dozens of times. “Innovation stopped because there’s no point to invent new source code — everyone copied everyone else’s.”

In this environment, competitive advantage can only come from two sources: a more cost-efficient business process, or a lower user acquisition cost. There is no third option. Product differentiation through novel code is largely unavailable. What remains is operational efficiency and growth efficiency — precisely the domains where AI creates real, sustainable competitive advantage.

Why Marketing Agencies Are Failing Web3 Founders

Martin spends considerable time in X Space #31 on the marketing agency problem — not because it’s a minor irritation, but because it represents a systematic misallocation of founder capital that directly prevents Web3 projects from reaching viability.

The parallel to early Web2 is precise. In the early 1990s, the gatekeepers of Internet marketing were traditional agencies who charged enormous fees to apply traditional advertising methods to a new medium — putting website URLs on billboards, in newspapers, on television. They made money, their clients generated website traffic, and essentially none of it converted because the targeting was non-existent and the experience wasn’t designed to convert. The agencies collected fees regardless of outcome.

The arrival of Google AdWords didn’t just reduce acquisition costs — it obsoleted the agency model. The agencies that survived became expert users of the new ad tech platforms. The ones that didn’t adapt closed. The technology did what the agencies were supposed to do, but better and cheaper.

“All these magic marketing agencies who are promising all the mana to the founders — they have all these different call strategies, all the different point strategies, or they’re using the crypto ads,” Martin says. “This is not converting. You can create a visitor flow to the website. But you as a founder are less interested in the visitor flow. You are interested about converting the visitor flow.”

The specific failure modes he identifies are common and recognizable to any Web3 founder: KOL (Key Opinion Leader) campaigns that drive traffic lasting 12–15 seconds before users bounce, with essentially zero conversion to transacting users. Coin ad networks (Coinzilla, Bitmedia) that are expensive, ad-blocker vulnerable, and disproportionately attract inexperienced users. Point/task systems that create artificial engagement metrics that don’t translate to protocol usage. All of these generate activity. None of them reliably generate transacting users at viable unit economics.

The solution isn’t to find better marketing agencies. It’s to adopt the ad tech infrastructure that makes targeting behavioral rather than demographic — the same shift that Google enabled in Web2. For a full breakdown of why KOL marketing specifically fails, see our guide on why influencer marketing isn’t working in Web3. For the alternative approach, see our guide to intention-based marketing in Web3.

ChainAware’s Five Live AI Products for Web3 Businesses

ChainAware’s response to these pain points is a suite of five live, production products — not white papers, not roadmap items, not beta features. These are systems that have been running for months to years, serving real clients, generating real intelligence. What follows is a detailed breakdown of each, drawing on Martin and Tarmo’s explanations in X Space #31.

1. AI Marketing Agents — 1:1 Behavioral Targeting

The marketing agent is ChainAware’s flagship growth product and the most direct implementation of the Web3 ad tech thesis. It solves the conversion problem — not the traffic problem — through real-time behavioral targeting at the wallet connection event.

The mechanism is a two-stage process that happens automatically every time a visitor connects their wallet to a DApp. Stage one: ChainAware’s predictive ML models analyze the wallet address and calculate the user’s behavioral profile — their DeFi experience level, risk tolerance, protocol history, and — most importantly — their predicted intentions: what are they likely to want to do next? Are they a yield farmer looking for the best APY? A trader hunting for leverage? A newcomer exploring DeFi for the first time? Stage two: based on those calculated intentions, the system generates personalized marketing messages — embedded content on the DApp’s website — that speak directly to what that specific user is trying to accomplish.

The contrast with conventional Web3 marketing is stark. Conventional: “Buy now and get 10% off” — the same message to every visitor, regardless of who they are or what they want. ChainAware: “You’ve been actively yield farming on ETH and BNB for 18 months, and you tend to favor low-risk positions. Here’s why our stable-yield vault might be exactly what you’re looking for.” The message is generated for that specific wallet’s profile. As Martin puts it: “You don’t know who the user is, but based on his blockchain history you can predict and create much higher attachment, much higher likeliness, much higher resonance.”

Tarmo makes a comparison to Amazon that every founder should understand: “If you go to Amazon, everybody sees it differently. It is calculated on the fly. Everybody sees his personalized UI what is generated for him.” This is what adaptive web interfaces look like in Web2. ChainAware brings the equivalent capability to Web3 — without cookies, without identity, using only public blockchain data.

The setup time is two minutes via Google Tag Manager — the same integration used for Google Analytics and other web tracking tools. No code changes required. The marketing agent begins generating personalized messages immediately. Founders can review, adjust, and refine the messages at any time — but even without any manual editing, the auto-generated content based on behavioral profiles substantially outperforms generic mass messaging in engagement and conversion metrics. A documented example of this in action: SmartCredit.io achieved 8x engagement and 2x conversions using ChainAware Growth Agents.

One aspect that Martin emphasizes repeatedly: the AI is embedded in the website, invisible to users. “To the outside it’s not visible that AI technology is behind there. It creates resonating messages for you.” This is a crucial design principle — not a chatbot that announces itself and that users dismiss, but ambient personalization that improves the user experience without friction. For the full technical guide to the analytics layer that powers this, see the Web3 Behavioral User Analytics complete guide. For the personalization philosophy, see why personalization is the next big thing for AI agents in Web3.

2. AI Transaction Monitoring Agent

The transaction monitoring agent addresses the fraud dimension of Web3’s structural problem — the 15% of TVL being lost to fraudulent activity. It operates as a continuous surveillance system that monitors wallet addresses connecting to or transacting with a DApp, flags behavioral changes that indicate emerging fraud risk, and delivers real-time notifications to platform operators.

The key architectural insight — one that ChainAware returns to consistently across their X Spaces — is the difference between forensic analysis and predictive monitoring. Most crypto security tools operate forensically: they document what has already happened, analyze blockchain history after the fact, and produce reports on completed fraud events. This is useful for investigation but useless for prevention, especially given blockchain’s irreversibility.

ChainAware’s monitoring is predictive: it evaluates behavioral patterns and predicts whether a wallet is trending toward fraud before any fraud occurs. Tarmo describes the mechanism: “You download addresses you want to monitor, you select notification mechanism, and the ChainAware agent just monitors it. As soon as an address does some strange behaviors you get notification. Strange behavior means the trust score of address is reduced — you get notification in real time.”

The quantitative context matters here. Fraud in Web3 — combining hacking, impersonation, scamming, and rug pulls — represents approximately 15% of TVL. This is not an edge case; it’s a systemic tax on every DeFi protocol. And it’s not inevitable: Web2’s credit card fraud rate was similarly approximately 15% of online transaction value in the early 1990s, before AI-powered transaction monitoring was introduced. Post-implementation, it dropped to well below 1%. This is the trajectory ChainAware is working to replicate for Web3.

The monitoring currently operates on Ethereum, BNB Smart Chain, and Polygon, with Telegram notifications being added to the existing API delivery system. For a detailed technical breakdown of how the monitoring agent works, the alert thresholds, and the integration path, see the complete Transaction Monitoring Agent guide and our AML vs transaction monitoring comparison.

3. AI Credit Scoring Agent

ChainAware’s credit scoring agent is the oldest product in the portfolio — the model has been live for more than four years, having originated in SmartCredit.io’s DeFi lending platform before being abstracted into a standalone service. It is the most mature, most backtested, and most validated AI model in the suite.

The core function is straightforward: given a wallet address, calculate a credit score that reflects the financial ability and creditworthiness of the person controlling that address. Tarmo describes it as the Web3 equivalent of a FICO score — “the same credit score what we calculate based on your on-chain data and your social data. We calculate a credit score and we monitor it.”

But as Tarmo carefully emphasizes, credit scoring in traditional finance is used for much more than lending decisions. “Credit score is not only used for borrowing lending — it’s used generally as an indicator of your financial ability. Higher credit score means your financial ability is higher. It’s a general indicator.” In Web3, this translates to what he calls “ABC filtering” — identifying your top A clients (high credit score, financially able), your B clients (moderate capability), and your C clients (low capability), and allocating resources accordingly. The Pareto principle operates here: “With 20% of clients you generate 80% of your revenue. If you know the credit score of your clients, you know which 20% to focus on.”

The monitoring aspect is equally important for lending protocols specifically: the agent continuously tracks credit score changes for existing borrowers. If a borrower’s credit score deteriorates — their financial behavior is showing signs of stress — the platform gets an early warning before any default occurs. This is the credit equivalent of the transaction monitoring agent’s fraud alerts: proactive intelligence that enables action before the problem manifests, not after. For the full technical guide, see the complete Web3 credit scoring guide and the Credit Scoring Agent guide.

4. Web3 User Analytics — Free Forever

Web3 User Analytics is ChainAware’s most accessible product — free forever for any Web3 platform, with no enterprise commitment required. It is also, arguably, the most immediately valuable product for founders who have never had reliable data on who their users actually are.

The problem it solves is fundamental. Most Web3 founders make strategic decisions based on assumptions about their users rather than data. They assume their DeFi protocol attracts experienced DeFi users. They assume their marketing is reaching the right audience. They assume their token holders are protocol users. Often, all three assumptions are wrong.

Martin gives a specific example from a DeFi platform that discovered through the analytics dashboard that their users — whom they assumed were DeFi-experienced participants — were actually predominantly low-risk traders who had minimal DeFi protocol experience. “They realized they had to change their marketing strategy. But if you want to change your strategy, first you have to know who your actual users are — not who is holding which token, but who is using which protocols.”

The dashboard shows eight dimensions of aggregate behavioral intelligence across all wallets connecting to the DApp: wallet intentions (what users plan to do next), experience distribution (Web3 sophistication level), risk willingness (how aggressively they engage with on-chain risk), protocol categories used, top specific protocols in user history, predicted fraud probability distribution, Wallet Rank distribution (overall quality of user base), and wallet age distribution (how long users have been in Web3). All of this is derived from public blockchain data with zero KYC, zero identity collection, and zero cookie dependency. For the complete walkthrough of all eight dimensions and how to use them, see our Web3 Behavioral User Analytics complete guide.

The integration is identical to the marketing agent: a Google Tag Manager pixel, no code changes, no engineering involvement. The dashboard begins populating with aggregate data within 24–48 hours of first wallet connections. This is your user base as it actually is — not as you assumed it was.

5. Marketing Strategy (Preferred Clients)

The fifth product is the most selective and the least publicly advertised: a comprehensive marketing strategy service available only to a small number of preferred clients. Martin is direct about why it’s not offered broadly: “It doesn’t make sense to offer it to everyone. There’s no benefit.”

The service combines both sides of the growth problem: acquiring a convertible visitor flow to the DApp, and converting that visitor flow into transacting users once they arrive. This is the distinction Martin returns to repeatedly — most marketing spend addresses only the first half (getting visitors to the website) while the second half (converting visitors to users) is ignored or addressed inadequately.

The approach uses ChainAware’s full behavioral intelligence stack: identifying which types of wallet addresses are most likely to be your high-value users, finding the acquisition channels that reach those wallets, and deploying the personalization infrastructure to maximize conversion once they arrive. It is the complete loop that replaces the traditional agency model — not just traffic generation, but traffic-generation targeted at wallets pre-qualified by behavioral profile.

The Roadmap: Base, Solana, and What’s Next

Martin outlines the near-term product roadmap across two dimensions: chain expansion and feature enhancement.

Chain Expansion

The marketing agent and user analytics currently run on Ethereum and BNB Smart Chain, with Base chain launching “in the next few days” at the time of the X Space, and Solana following shortly after. “Because on Solana there is so much activity, we’re launching it as well on Solana.” This brings the supported chains for the growth products to: ETH, BNB, BASE, and SOL — covering the four highest-activity chains for DeFi and DApp activity in 2025.

The fraud detection and transaction monitoring models already cover a broader set: ETH, BNB, BASE, HAQQ, SOL, TON, TRX, and POL — eight chains in total for the full behavioral intelligence stack. The Prediction MCP server exposes all of this intelligence as callable tools for AI agent integration.

Feature Enhancement: Telegram Notifications

The transaction monitoring agent is adding Telegram notification support alongside the existing API delivery. This removes the need for engineering work to receive fraud alerts — instead of building a notification system, compliance contacts and COOs can simply receive direct Telegram messages when wallets crossing fraud thresholds connect to or transact with their platform.

Compute Infrastructure

Tarmo mentions ChainAware’s compute infrastructure partnership, which is relevant context for understanding the scale of what these models require: “We are in Google Cloud Web3 Startup Program. We have enormous compute power from Google and this is how we can do all these calculations.” Predictive behavioral AI at the scale ChainAware operates — 14M+ wallet profiles, continuous retraining, real-time inference — requires significant compute infrastructure that most startups couldn’t self-fund. The Google Cloud partnership enables the daily model retraining and real-time prediction latency that make the products practically useful. For more on why compute scale matters for model quality, see our guide on AI-powered blockchain analysis: machine learning for crypto security.

Crossing the Chasm: How Web3 Gets to Exponential Growth

The X Space #31 concludes with the big picture framing that gives the entire product roadmap its context: what needs to happen for Web3 to cross the chasm into exponential growth?

Martin and Tarmo’s analysis, grounded in their observation of Web2’s growth trajectory and their years of building in Web3, converges on a specific thesis: the crossing-the-chasm moment for Web3 will be enabled by exactly the same two technologies that enabled it for Web2. Not by a sudden surge in public interest. Not by a killer app that everyone suddenly wants. Not by regulatory clarity. By two specific infrastructure technologies that remove the two specific blockers that are currently holding the ecosystem back.

Technology 1: Predictive fraud detection at scale, integrated across platforms, reducing Web3’s 15% fraud rate toward the sub-1% rate that Web2 achieved after AI-powered monitoring was deployed. This restores user trust and removes the “I’ll get burned if I engage” fear that drives users out of the ecosystem faster than organic growth can replace them.

Technology 2: Behavioral ad tech for Web3 — 1:1 behavioral targeting based on on-chain wallet data, reducing the cost of acquiring a transacting user from the current $1,000–$3,000 toward the $15–$30 that Web2 achieves. This makes the unit economics of Web3 platforms viable and enables sustainable growth rather than treasury-subsidized user acquisition.

Tarmo’s summary: “JNAware is the company which has technologies which brought Web2 to exponential growth, and we can bring also Web3 to exponential growth.” This isn’t marketing language — it’s an architectural thesis grounded in specific historical analysis and specific technology claims. The technologies that solved Web2’s problems exist. They work. They’re running in production. The question is how quickly Web3 projects adopt them.

For the broader context of where AI agents fit into the long-term evolution of Web3, see our articles on the Web3 agentic economy and why 90% of connected wallets never transact — and how AI agents fix it.

Comparison: ChainAware vs Traditional Web3 Growth Approaches

Approach	What It Delivers	Cost	Conversion Quality	Scalable	Targeting
KOL / Influencer Marketing	Short-term traffic spikes	$5K–$50K+ per campaign	Very Low (12–15 sec sessions)	No	None — mass broadcast
Crypto Ad Networks (Coinzilla etc.)	Banner impressions	High CPC, ad-blocked	Low — attracts newcomers	Expensive	Basic demographics
Airdrop / Point Systems	Wallet connections	Token treasury dilution	Very Low — farmers, not users	Yes but degrades	None
Smart Contract Audits (trust signal)	Code-layer trust badge	$20K–$200K+	N/A — not a growth tool	One-time	None
ChainAware Marketing Agents	1:1 personalized conversion	Subscription, 2-min setup	High — intention-matched	Fully automated	On-chain behavioral targeting
ChainAware User Analytics (free)	Actual user behavioral data	Free	N/A — intelligence tool	Continuous	Aggregate behavioral profiling
ChainAware Transaction Monitoring	Fraud prevention + trust	Enterprise subscription	Improves by filtering fraud	Fully automated	Individual wallet behavioral monitoring
ChainAware Credit Scoring	Borrower quality + ABC filtering	API subscription	Improves by filtering low-quality	Continuous	Individual creditworthiness scoring

The fundamental difference in the table is targeting. Every traditional Web3 growth approach operates without behavioral targeting — it reaches people, but not the right people at the right moment with the right message. ChainAware’s approach targets based on what each specific wallet is likely to want next, derived from their actual on-chain history. This is the difference between billboard advertising and Google AdWords — the same conceptual gap that defined the transition from Web1 to Web2.

For an in-depth comparison of Web3 analytics and growth platforms, see our Web3 analytics tools comparison and Web3 growth platforms compared.

Frequently Asked Questions

Why is Web3 user acquisition cost so much higher than Web2?

Web2 has decades of behavioral targeting infrastructure built on top of identity-linked data (cookies, login IDs, device fingerprints) that enables highly precise user targeting. Web3 currently lacks equivalent infrastructure — most growth campaigns use mass broadcast methods (KOLs, crypto ad networks, airdrop campaigns) that generate traffic but not behaviorally-qualified transacting users. ChainAware’s behavioral targeting infrastructure closes this gap by using on-chain wallet data to predict user intentions and deliver resonating messages, reducing acquisition cost toward Web2 levels.

How is ChainAware’s fraud detection different from AML screening?

AML (Anti-Money Laundering) screening is rules-based — it checks whether a wallet has interacted with sanctioned addresses, mixers, or other flagged entities. The rules are public and sophisticated fraudsters can work around them by using clean funds. ChainAware’s fraud detection is predictive ML — it identifies behavioral patterns that predict future fraudulent activity, even from wallets with no AML flags. 98% accuracy on held-out test data. Predicts fraud before it occurs, not after. See the complete Fraud Detector guide for full methodology.

What does the marketing agent actually show users?

The marketing agent generates embedded content — messages, callouts, feature highlights — on your DApp’s website that are personalized to each connecting wallet’s behavioral profile. Think of the difference between a generic “Earn up to 12% APY” banner and a message tailored to a wallet that has been actively yield farming on Aave and Compound for two years, showing moderate risk tolerance: “For experienced yield farmers: our 3-month fixed-rate vault currently offers competitive stable returns, with no liquidation risk.” The second message resonates because it matches what that specific user is actually looking for. The messages are auto-generated, reviewed/edited by your team if desired, and embedded into your existing website without UI changes.

Is the Web3 User Analytics dashboard really free?

Yes — free forever for existing integrations. Martin is explicit: “We offer it for free for any Web3 platform if you want to use it. Free forever.” The free tier shows aggregate behavioral data across your user base across all eight dimensions. Individual wallet targeting (the marketing agent) is an enterprise subscription. The free analytics tier is ChainAware’s goodwill contribution to the Web3 ecosystem — giving every founder the data they need to understand their actual users, rather than operating on assumptions. Subscribe at chainaware.ai/subscribe/starter.

Which blockchains does ChainAware support?

At the time of X Space #31: ETH and BNB for the full product suite, with Base launching imminently and Solana to follow. The fraud detection and transaction monitoring models cover a broader set: ETH, BNB, BASE, POL, SOL, TON, TRX, and HAQQ — 8 blockchains total. Check chainaware.ai for the current chain coverage across each product.

How does ChainAware target users without knowing their identity?

All intelligence is derived from public blockchain transaction data. ChainAware never requires KYC, never collects personal information, and never links wallet addresses to real-world identities. The behavioral profile — experience level, risk tolerance, protocol history, intentions — is calculated entirely from the public on-chain transaction history associated with the wallet address. This is actually more privacy-preserving than Web2 targeting (which requires identity-linked data) while being more accurate for Web3 use cases (because on-chain behavior is a more direct signal of DeFi intent than browsing history or demographics).

This article is based on X Space #31 hosted by ChainAware.ai co-founders Martin and Tarmo. Watch the full recording on YouTube ↗ · Listen on X ↗. For questions or integration support, visit chainaware.ai.