ChainAware co-founder Martin covers the full platform origin story and AI architecture in this ChainGPT Pad AMA. ChainAware emerged from SmartCredit.io credit scoring — credit scoring required fraud scoring, fraud scoring proved more valuable in DeFi, rug pull detection followed. The accidental roadmap that became a 32-agent behavioral intelligence platform.
ChainAware co-founder Martin joins Magic Square to discuss Web3 AdTech and fraud detection for the real economy. Covers ChainAware’s origin from SmartCredit credit scoring through to fraud detection, rug pull prediction, wallet auditing, and Web3 AdTech — and why custom AI models, not LLM wrappers, are the only defensible IP moat in Web3.
ChainAware co-founders Martin and Tarmo join Datai and ChainGPT Labs to map what Web3 AI agents actually are and what they already do in production. Covers ChainAware’s two live production agents — Web3 marketing agent and behavioral fraud detection agent — alongside Datai’s data infrastructure and ChainGPT’s incubation model.
Every VASP needs transaction monitoring — but most rely on static tools and manual review that miss behavioral fraud. Based on an X Space recap, this guide covers how ChainAware’s AI Transaction Monitoring Agent runs 24/7, integrates via GTM with no engineering, and acts automatically on detection via shadow ban, full ban, or Telegram alert.
Web3 fraud costs the industry billions annually and keeps mainstream users away. Static rule-based detection systems fail — bypassed within days, 30–70% false positive rates. This guide explains how AI-based predictive fraud detection works, why it is the missing key to mainstream Web3 adoption, and how ChainAware’s ML models achieve 98% accuracy in real time.
Static smart contract analysis fails against professional rug pull operators who deliberately write clean code. Behavioral AI catches what code scanners miss — by reading the on-chain history of the people behind the contract. This guide explains why behavioral prediction beats static analysis for rug pull detection and how ChainAware’s V3 model achieves 90.1% accuracy.
Web3 cannot grow at scale without solving two structural problems simultaneously: fraud and mass marketing. X Space #4 with ChainAware co-founders Martin and Tarmo covers why the 2–3% annual DeFi hack rate has held constant for four years despite billions invested in security — and how real-time fraud detection combined with 1:1 marketing breaks the cycle.
Web3 is now behind Web2 on the innovation curve — mass marketing at 0.1% conversion vs Web2’s 10–30% with intention-based targeting. X Space #2 with ChainAware co-founders Martin and Tarmo explains how AI and Web3 convergence closes this gap and brings blockchain adoption back to the front of the curve.
DeFi copied the wrong lending model and the wrong security model. X Space #1 with ChainAware co-founders Martin and Tarmo covers how a Byzantine trust layer fixes both — replacing variable rates with predictable fixed-rate lending and replacing backward-looking AML forensics with real-time predictive fraud detection.