After evaluating Claude Pro and Perplexity Pro alongside Suprmind, we’re canceling both
DCI helps track exactly where data comes from, so you have a clear audit trail for decisions. It’s not magic but gives you a reliable way to check the origins of info, cutting down guesswork when you're verifying forecasts or strategy inputs
Model disagreement helps catch risky inputs by highlighting cases where predictions conflict. By tracking metrics like ensemble variance or prediction margin, you can spot uncertain examples early
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DCI helps keep your data honest by tracking its full journey—who touched it, when, and how. For strategy and audit pros, this means a clearer provenance record to trust insights and spot red flags early
We decided to keep Claude Pro and Perplexity Pro alongside Suprmind to leverage orchestration over simple aggregation. Combining these tools lets us sequentially compound insights, using disagreement as a feature for deeper analysis
Model disagreement helps spot risky inputs by measuring how much predictions differ, using metrics like ensemble variance or margin. When models disagree a lot, that signals uncertainty or edge cases
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Model disagreement, measured by metrics like ensemble variance or entropy, helps spot risky inputs where predictions don’t align