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Gather Evidence Across Tiers
Draw evidence from global and continental sources, regional and national sources, and ground-level or community sources for each insight.
The ADZA Confidence Framework evaluates every insight OpenTrace produces by triangulating evidence across global, national, and ground-level sources. Every output carries a clear confidence signal, so organisations can see not just what an insight says, but how strong the evidence behind it actually is.
ADZA evaluates each insight by triangulating evidence across tier coverage, source alignment, freshness, and granularity. Users understand not only what an insight says, but how much confidence they can place in it and why.
HOW ADZA CONFIDENCE MODEL WORKS
Draw evidence from global and continental sources, regional and national sources, and ground-level or community sources for each insight.
Assess how well the evidence spans the right tiers, and whether sources broadly agree in direction, pattern, and magnitude.
Account for how recent the evidence is, and how closely its resolution matches the claim being made.
Present each insight with a clear confidence level, an explanation of what shaped it, and its limitations, never a bare, unexplained answer.
Evaluates whether evidence is present across global, national, and ground-level sources, not just one.
Checks whether the available sources broadly agree in direction, pattern, and scale before confidence is assigned.
Confirms the resolution of the evidence actually matches the resolution of the claim. National data is not used to support a village-level claim.
See exactly how strong the evidence is behind planning, food security, and resource allocation insights before committing public resources.
Prioritise interventions using confidence-scored evidence that shows where data is strong and where it is still developing.
Attach a clear confidence signal to every lending, sourcing, or investment decision, so exposure is never based on an unexplained number.
Evaluate datasets, models, and analytical outputs using explainable, tier-based confidence signals built for rigorous research use.
See a plain-language signal for how much to trust each piece of guidance delivered through Ask ADZA, before acting on it.
Make public decisions with clearer evidence, risk awareness and confidence levels.
Prioritise interventions using intelligence that shows its evidence strength and uncertainty.
Assess agricultural risk and validate claims with transparent confidence attached to every signal.
Use explainable confidence signals to evaluate datasets, models and analytical outputs.
Get plain-language confidence signals on the guidance they receive through Ask ADZA.
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