Built for policy, risk, and strategy teams who need more than a confident-sounding paragraph — they need to know how much to trust it.
Structured analysis for uncertain, high-stakes decisions.
DECS runs your question through a disciplined, multi-agent analysis process — surfacing what's known, what's contested, and a confidence score that reflects how well the evidence actually supports the answer, not how likely the outcome is — so you're not relying on a single model's best guess.
Policy, risk, and strategy teams increasingly need to make judgment calls under real uncertainty — not steady, predictable change, but situations where multiple forces are pulling against each other and the outcome could shift by the day. DECS is built to read exactly that kind of entanglement: it identifies what regime we're actually in right now, maps how the competing forces relate, compares it to historical precedent, and actively looks for reasons its own conclusion might be wrong — before presenting a single, confidence-scored answer. A steady situation with no real tension between actors isn't what DECS is built to read; an entangled one is.
Frame the situation you're trying to understand.
Separate agents examine current signals, the underlying situation type, how different forces are entangled, and relevant historical precedent.
Before you see a result, a dedicated process looks for weaknesses, bias, or overconfidence in the analysis — and adjusts the confidence score accordingly.
A plain-language summary up top, full technical analysis available below it, and a transparent sense of how much weight the conclusion can bear.
Technical terms
View the full glossaryA few terms you may see in your results.
- Agent
- One of seven specialized AI components that each examine a different part of your question.
- Critic Agent
- Challenges the conclusions and points out possible mistakes.
- C_final Score
- Overall confidence in the conclusion (0 to 1). Example: 0.35 = 35% confidence (high uncertainty).