The builders behind Scanna

Forecasting uncertain future events is one of the most valuable capabilities an artificial intelligence system can possess. Executing this reliably across geopolitics, finance, and macroeconomics requires combining disparate data sources and reasoning under intense uncertainty. Clean, normalized, and appropriately scoped data is a necessary input for useful analysis, especially when source streams are large or noisy.

Scanna provides a market-intelligence data feed through a Model Context Protocol (MCP) server and REST API. It converts supported source data into structured, token-efficient telemetry and exposes analytical fields for downstream tools.

The data bottleneck challenge

Prediction markets generate extensive streams of real-time public data; however, this information is structurally unsuited for direct processing by large language models for three primary reasons:

• Context window overflow: raw on-chain logs are highly verbose, and direct input into a model rapidly exhausts its context window.
• Attention degradation: even when context capacity is theoretically adequate, empirical evidence indicates significant attention degradation in long-context scenarios. As a result, critical market events embedded deep within continuous data streams are often missed.
• Dual timeline discrepancy: prediction market liquidity reacts to irregular, human-paced off-chain events such as breaking news, regulatory decisions, and real-world incidents, while order books update on a much faster on-chain blockchain timeline. Most agent architectures cannot monitor both environments concurrently, leading to reliance on outdated snapshots of the live state.

Although raw data contains essential insights, effective utilization requires specialized infrastructure.

Core infrastructure

Scanna accelerates decision-making by transforming fragmented prediction data into unified, clean outputs, integrating available off-chain context with on-chain analytical data.

• On-chain forensic analytics: monitors live blockchain events, conducts in-depth wallet analysis, identifies smart money positioning, and performs entity clustering to map prominent trader networks across Web3 platforms such as Polymarket.
• Off-chain structural ingestion: collects real-time web content using continuous natural language monitoring and semantic crawlers to extract concise, token-efficient highlights.
• Dynamic market telemetry: produces continuous heat scores based on whale activity, multifactor signal analysis, and rigorous risk assessments across all monitored venues.
• Cross-venue inefficiency detection: aggregates and normalizes order books, pricing, and metadata across platforms, including Polymarket and Kalshi, to automatically identify structural arbitrage opportunities and pricing divergences.
• Model and signal orchestration: combines normalized data, analytical factors, and available model outputs into structured responses.
• Autonomous billing infrastructure: incorporates usage-based machine monetization protocols, including x402 standards, as well as flat-rate subscription tiers. This approach eliminates payment friction, setup delays, and the need for human-managed API keys in autonomous workflows.

Target ecosystem

Scanna is purpose-built for professionals, organizations, and AI-powered systems that use prediction-market intelligence. Its solutions specifically address the needs of quantitative funds, trading professionals, research analysts, enterprise strategists, and developers of autonomous trading agents.

• Quantitative funds and trading professionals: Scanna provides normalized market data and analytics to help trading teams, quants, and portfolio managers inspect pricing differences and market conditions.
• Analysts and researchers: Scanna provides historical and real-time normalized telemetry to data scientists, academics, and financial researchers seeking robust tools for cross-market analysis, anomaly detection, and correlation studies.
• Enterprise decision makers: Scanna equips corporate executives, risk management professionals, and strategic planners with prediction-market data and model outputs that can be considered alongside other sources.
• Autonomous trading agents: Scanna is designed for developers and operators of AI agents that use x402 stablecoin micropayments to automate data procurement, enabling these agents to access intelligence-as-a-service without human intervention.

Strategic timing

Three structural shifts are currently converging. First, prediction markets have evolved from niche activities into high-liquidity financial venues, serving as structural engines of global market consensus. Second, autonomous AI agents capable of independent execution are being deployed in production environments. Third, machine-native payment standards are emerging concurrently.

Scanna supplies supported prediction-market data and analytical fields through interfaces designed for automated clients and human developers.

We are engineering the intelligence layer for the agentic economy, providing structured telemetry for systems that analyze prediction markets.

You will own the systems that solve the synchronization gap between hyper-fast blockchain liquidity and off-chain reality. Your core responsibility involves engineering the complete lifecycle of Scanna’s capabilities. This includes building model-serving integrations, designing data pipelines, and implementing machine-native x402 settlement rails.

We are looking for talent that pushes the frontier of AI and decentralized finance. The technical foundation requires deep proficiency in resolving LLM context bottlenecks, deploying on-chain forensic analytics, and engineering scalable AI tooling. We prioritize practical execution and a rigorous understanding of how autonomous models interact with live capital over traditional finance backgrounds.

We bypass traditional resumes. Prospective candidates must directly submit evidence of their capability to the founders. Compile a technical portfolio that demonstrates practical execution and exactly how you think. Submit a deployed data pipeline, a novel ML architecture, or an autonomous trading agent with verifiable credentials via auditable smart contracts. We read and verify everything.

Scanna has developed a strong foundation in autonomous forecasting and structural arbitrage for autonomous trading agents. Strategic partners interested in advancing real-time intelligence in prediction markets and contributing to Scanna’s ongoing growth are invited to contact the founding team for additional information.