DEMO

Watch an AI that refuses to guess

Three scenarios showing traceable symbolic reasoning — plus one showing what honest AI looks like when evidence is insufficient.

Every conclusion is provable. Every refusal is principled.

The Difference

Same question. Different architecture.

Typical LLM

Input: "Store #7 revenue dropped 15%. What's the cause?"

"Based on general trends, it's likely due to seasonal factors and possibly increased competition in the area. I'd recommend running a promotion and reviewing your pricing strategy." ⚠ No data consulted. No causal chain. Confident guess presented as analysis.

Jachin

Input: "Store #7 revenue dropped 15%. What's the cause?"

"Cannot determine causality. Weather API not connected. No competitor data available. 1 seasonal match found (n=1, insufficient for deduction). Suspending analysis." ✓ Honest refusal. Tells you exactly what data is missing and what to do next.

See it on your data

Ready to test your own logic?

The examples above use pre-computed reasoning traces. Schedule a session to see Jachin run live symbolic verification on your domain.