The information layer for autonomous trading and decision-making.
Marking turns fragmented crypto data into verifiable information autonomous trading systems can buy and consume.
Autonomous software can access data. It cannot easily assemble trustworthy information.
An agent needs the information required to decide.
Is there an executable BTC arbitrage opportunity right now?
What is the current BTC market-making state?
Fair-value inputs · imbalance · volatility · liquidity · funding
Did this event happen?
Sources · evidence · resolution policy · attestation
Marking returns trusted information. The agent owns the decision and execution.
Marking turns fragmented data into decision-ready information.
The company-building milestone: Convert technical proof into PMF learning: weekly external usage, one publisher pilot and measured unit economics.
The same information layer can power many autonomous strategies.
Available today: canonical crypto prices and one event-resolution prototype. Strategy-specific intelligence products are next—not claimed as complete.
One testnet payment unlocked a signed live stream.
Certification report evidence. Base Sepolia testnet. Three latency samples—not a percentile or geographic SLA. Payment and access are implemented; decision and execution remain with the consumer.
Trust travels with the information.
The recipient verifies the object—not a screenshot, dashboard or database assertion.
Representative implemented schema fields. Publisher-origin signatures and broader intelligence envelopes remain planned.
The information path stays fast; payment and durable state stay separate.
200 feeds · 1,550 obs/s · 0.051471 ms P95 source-received → canonical-signed
60-second scoped local Rust benchmark; 93,001 observations. Not production, geographic or consumer-delivery certification. Rust remains shadow-only.
Marking sits above fragmented information sources.
A narrow wedge with network potential
Start with paid decision-ready information for agents. Demand creates publisher value; first-party provenance then deepens the product.
provide valuable raw and canonical data
assembles decision-ready information for autonomous consumers
discover → understand → pay → consume → verify → decide
Three product types match three information jobs.
Raw and canonical information
BTC/USD for 30 days = $51.84Market state, volatility, liquidity, funding and risk
Initial pricing hypothesisA specific answer: opportunity, funding, liquidation or outcome
Initial pricing hypothesisNo revenue, paying customers or validated willingness-to-pay is evidenced. Provider rights and fees remain release gates. Monthly plans may coexist with machine-native usage pricing.
Every new consumer can make the information network more valuable.
Rapid technical execution exists; the next cycle must produce repeated external demand.
COMMITMENT → Run a disciplined PMF sprint around paid, verified information sessions.
The core information infrastructure exists. The product layer is expanding.
Turn autonomous information infrastructure into a scalable company.
Technical execution: deployed preview + certified payment flow + scoped 200-feed local benchmark.
14+ years across JPMorgan Chase and Amazon; former VP Technical Program Manager in Digital Markets Execution & Technology; 4+ years in blockchain.
About 14 years across JPMorgan Chase, Amazon and crypto GTM; institutional-client, ETF-marketing and customer-acquisition experience.
FOUNDING TEAM → PRODUCT & TECHNOLOGY + GTM & OPERATIONS