Cenva continuously researches, forecasts, and updates high-stakes questions with evidence, probabilities, and a public track record.
Cenva turns recurring uncertainty into a tracked, measurable workflow: monitor what matters, forecast with evidence, update when the world changes, and score outcomes over time.
Add companies by ticker, ask your own questions, generate question sets from a document, or start with curated topic packs. Track anything your team revisits.
Multiple specialized AI agents independently research, critique, and calibrate each forecast.
Forecasts re-run when material evidence surfaces. Every probability change is timestamped, sourced, and explained.
Every resolved question is scored against reality. The system publishes its hits and misses, and calibration tightens with each cycle.
Cenva is not a one-off chatbot. It is a persistent system for following important questions over time — including questions that never appear on public prediction markets.
Every strong AI claim should be backed by measurement. Cenva publishes public scorecards, recent forecasts, and resolved postmortems so you can judge the system on evidence rather than marketing.
Explore ProofOur proof page measures forecast accuracy. These samples show the research, monitoring, and scenario work the system produces for companies and questions that never appear on public prediction markets.
Cenva is generalizable across domains where uncertainty is expensive and decisions repeat.
Always-on company monitoring, scenario analysis, and probability estimates that benchmark against market consensus. Edge-first, not narrative-first.
A live probability layer across your exposure map. Regulatory shifts, counterparty risk, and liquidity events surfaced with evidence before they hit the tape.
Competitors, market shifts, and strategic bets tracked with calibrated probabilities. Structured evidence replaces gut-feel slideshows.
Geopolitical developments, regulatory trajectories, and macro policy questions — tracked with probabilities that update as the world does.
Calibrated probabilities with confidence intervals, key drivers, and edge vs. market consensus.
Every probability change comes with the evidence that triggered it. Timestamped and sourced.
Deep research reports with executive summaries, competitive analysis, and bull/base/bear scenarios.
Public-safe examples of company research: scenarios, signals, watchpoints, and auto-generated questions for tracked companies.
Curated sets of forecastable questions around macro themes, sectors, or strategic bets — with calibrated probabilities.
What we forecast, what actually happened, and what the system learned. Hits and misses both published.
Public track record with Brier scores, calibration curves, and domain-level breakdowns. No cherry-picking.
Every forecast comes with explicit probabilities, cited evidence, timestamped updates, and scored outcomes. The system covers questions that public markets don’t price — and builds a verifiable track record on the ones they do.
PhD in AI-driven smart-contract security. Former CTO at Traverse (Australian Red Cross) — self-sovereign credentials & onchain governance. Lead author of Detect Llama, an LLM that beats GPT-4 at vulnerability detection.
Leads Product at Coinbase — blockchain platform & staking. Runs crypto infrastructure and product strategy across Coinbase's highest-growth verticals.