Systems

How these systems work

Everything fails closed

"Fail closed" means the system treats silence as safer than a guess. If a code review can't find your policy document to back up a finding, the finding gets killed instead of posted. If a trading signal can't pass eight independent safety checks, no money moves. If a financial analysis can't verify its data sources, it tells you what it doesn't know instead of making something up. Every system is designed so that failure looks like "I don't have enough information" rather than "here's a confident wrong answer."

The AI doesn't get to make things up

Every system has a verification layer that checks the AI's work against real data before anything reaches you. In code review, a second AI model re-reads every finding against your actual documentation. If it can't prove the finding from what's written, the finding dies. In trading, the AI's stated numbers get compared against math that was computed independently. If they don't match, the trade is rejected. In financial analysis, every claim must trace back to a specific data source or it gets flagged as unverifiable.

You can tell where the answer came from

Normal AI confidence scores tell you how sure the model is. That's not very useful because AI models are confidently wrong all the time. These systems tell you why the model is confident. Every finding is labeled: either it came from your actual documentation (with a citation you can check), or it's the AI's own suggestion (clearly marked as such). The traffic-light system (red means stop, yellow means look, green means go) is built on top of these labeled findings, not on how sure the AI feels about itself.

Math first, AI second

In every system, the straightforward computational work happens first using regular deterministic code that can't hallucinate. The AI only gets involved when there's something that requires judgment or interpretation, and even then, it works from the math's output as its starting point, not from raw data. In the trading system, technical indicators are computed locally in microseconds. The AI only activates when the math says "something interesting is happening here, figure out what it means." This keeps the AI on a leash: it interprets verified facts rather than inventing its own.

The system can't teach itself bad habits

The trading system improves over time, but it does so under strict rules. Every lesson it learns has an expiration date, a minimum number of observations before it takes effect, and limits on how much it can change at once. It can't lock in a bad lesson permanently based on a small run of bad luck.

It learns from what it didn't do, not just what it did

Most systems only learn from their mistakes. This one learns from four things at once: trades that worked (keep doing this), trades that didn't work (stop doing this), trades it rejected that would have lost (good call, the safety checks are working), and trades it rejected that would have won (the safety checks might be too strict here). Every rejected trade is tracked with the same level of detail as an executed trade, so the system can compare what it did against what would have happened if it hadn't said no.

It knows where the problem is, not just that there is one

The four types of feedback feed into a diagnostic system that answers a specific question: if something is going wrong, is it because the signal selection is off, the safety gates are too tight, the execution timing is bad, or the whole market has changed? A system that knows it's losing money is basic. A system that can tell you why, and whether the fix is in the filters, the strategy, or the market conditions? That's diagnostic intelligence.

No feedback loops

None of these systems are allowed to use their own previous output as evidence for their next decision. The AI can't retry a failed operation on its own. It can't treat something it said earlier as a fact. It can't build on top of its own guesses. Every decision starts from verified data, not from the AI's prior conclusions. This prevents the kind of compounding errors where a small early mistake snowballs into a catastrophic one.