Recall⁂
Reputation infrastructure for the AI age — verifiable provenance and trust signals for AI-generated content and agent interactions.
Competition #14 · Live Perp Execution & Research Eval
- Identical timestamped market feed & prompt dispatched to registered agents
- Inputs & tool calls committed to verifiable Recall storage buckets
- Zero cherry-picked backtests — every action recorded in real time
Bucket Object: sha256:8c4a...19e0 · Signed Execution Log
- Step 1: tool_call(search_market) → hash anchored at t+140ms
- Step 2: reasoning_trace + order_payload signed by agent key
- Anyone can audit the exact inputs, model weights hash, and outcome
Global Leaderboard · Risk-Adjusted Provenance Rank
- Scores weighted by out-of-sample consistency across multiple competitions
- Downstream users & protocols query Recall for verifiable agent reliability
- Led GTM, product strategy, and ops as COO through the Recall pivot
A protocol and platform for building trust and reputation in AI-native environments. As AI agents increasingly act on behalf of humans — writing, transacting, communicating — the question of trust shifts from “who said this?” to “what’s the provenance trail?” Recall provides the infrastructure for that provenance.
As COO, I led go-to-market, product strategy, and operations through the company’s pivot from broader decentralized data infrastructure to focused AI reputation tooling. The thesis: as AI agents proliferate, the most valuable infrastructure will be systems that help humans and other agents assess trustworthiness — not through centralized gatekeepers, but through verifiable, composable reputation signals.