Gnist Credence
Some questions are too tangled to hold in your head — "Should I buy this apartment?", "Will this startup reach its revenue target?", "Will this market resolve yes?" The factors interact, the assumptions hide, and the reasoning gets buried in a long chat thread.
Gnist Credence helps you and an AI assistant build a shared, explicit, probabilistic model of the problem instead of leaving the reasoning trapped in someone's head. It gets the thinking on paper — an object you can inspect, challenge, update, and improve over time.
It is not an oracle. The value is not that the AI knows the answer; it is that the AI helps construct and maintain a structured picture neither of you could reliably hold alone.

You start with a plain-language question, not a diagram. From there, Credence builds an Inquiry — a structured investigation that holds the moving parts of your reasoning:
- Beliefs — probabilistic statements that feed the question, each tagged as yours or the AI's
- Evidence — information that should move those beliefs, traceably linked
- Key drivers — the assumptions your conclusion is most sensitive to
- Update history — an inspectable trail of every change: what moved, why, and who changed it
The AI is active but never overconfident: it surfaces hidden assumptions and proposes structure, but never silently overwrites your beliefs or collapses the uncertainty into a single confident number.

Credence is AI-native first. The primary interface is an MCP server your AI client connects to directly, with a REST counterpart for scripting and testing. Start anonymously with a lightweight token, then attach your work to an account later without losing a thing.
Our first proving ground is prediction-market forecasters — they already think in probabilities and can measure their own calibration over time. From there: richer evidence handling, sharper structure, and portability across AI clients.
The MVP is live. Pricing is still finding its shape — free while we're early.

Connect your AI client and start turning a fuzzy question into an explicit, shared model you can actually reason with.
It is live now. Your feedback shapes what ships next.
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