Decisions on Autopilot
Zeraviard applies predictive data analysis to investment and business decisions, so you can act on structured signal rather than guesswork — whichever time zone you happen to be working from.
Zeraviard applies predictive data analysis to investment and business decisions, so you can act on structured signal rather than guesswork — whichever time zone you happen to be working from.
Every model run produces a timestamped entry. The figures below are illustrative of the format used in our public log, which updates on each completed forecasting cycle.
| Model | Window | Predicted Accuracy | Status |
|---|---|---|---|
| Equity Volatility Model | 30-day | 84.2% | Verified |
| Crypto Signal Engine | 7-day | 77.6% | Verified |
| B2B Arbitrage Screener | 14-day | 81.9% | Verified |
Each log entry records the model version, input window, and outcome at the point of resolution, so figures cannot be revised after the fact.
Zeraviard combines three layers of modelling. Each layer is designed to be explainable, so recommendations can be checked rather than taken on faith.
Forecasts are generated using Bayesian inference, updating prior probabilities as new market data arrives. This produces a confidence range rather than a single fixed prediction, reflecting genuine uncertainty.
High-frequency data ingestion pulls from market feeds and public filings, scoring shifts in sentiment as they occur. The system flags divergences between sentiment and price before they fully resolve.
Recommendations are filtered through risk-mitigation parameters set by the user, including maximum exposure and drawdown limits, before any output is surfaced for action.
The platform is designed around intermittent connectivity. Set your parameters once, and the analysis continues to run whether you're online or mid-flight.
Link your brokerage, exchange, or business data sources. Zeraviard ingests historical and live data without requiring manual uploads.
Define your risk tolerance, target markets, and exposure limits. These parameters govern every recommendation the model produces.
The engine continues to process incoming data and refine recommendations in the background, logging every decision for later review.
The same modelling approach is applied differently depending on the market and the question being asked.
Daily price movement is dominated by short-term noise. Zeraviard isolates recurring structural patterns from random fluctuation, surfacing only the signals that meet a confidence threshold.
Crypto markets react sharply to sentiment. The platform tracks sentiment velocity alongside price, helping to distinguish a genuine shift from a short-lived spike.
Cross-market price discrepancies close quickly once spotted manually. Automated screening flags viable gaps while they still have practical value.
Zeraviard was designed around a simple constraint: decisions often need to be made when the person making them is not at a desk. The platform separates the analysis layer from the execution layer, so recommendations are ready whenever you next check in.
Every output is traceable back to the data and parameters that produced it, which is why the performance log exists as a core part of the product rather than a marketing addition.
Request access to the current parameter templates and the full transparency log, then adapt them to your own risk profile.
Risk disclosure: Trading and investment decisions carry risk, including the risk of capital loss. Past performance and simulated or historical figures shown in the transparency log are not a reliable indicator of future results. Zeraviard provides data analysis tools and does not offer regulated financial advice. You should seek independent financial advice before making investment decisions, in line with guidance from the UK Financial Conduct Authority.