DDE — Decision & Data Evidence Audit Engine

People are using AI to write reports, market analysis, business plans, and backtests. The problem is that AI can make weak conclusions look polished. A report may sound confident, include numbers, and still have missing baselines, vague sources, or conclusions that are not actually supported.

DDE audits AI-generated reports and backtests before people trust them. It separates what can be referenced, what needs more evidence, and what should not be trusted yet. It checks evidence chains, unsupported precise numbers, baselines, time drift, and overfitted backtest results.

AI tools are moving faster than people’s ability to verify their outputs. More founders, small businesses, and investors are using AI for decisions, but most do not have a simple way to check whether the result is reliable before acting on it.

The hard part is not generating another answer. It is building an audit process that can reject convincing-looking outputs, including my own. DDE has frozen tests, regression checks, adversarial cases, and records of failed signals, so it is designed to audit conclusions instead of selling confidence.

AI output auditing, backtest validation, evidence chains, trust before adoption