@LiorOnAI
Every AI answer you trust right now has unchecked logic. Most tools retrieve text and summarize it, but none of them verify whether the output is actually true. One wrong source in a financial memo and your credibility is gone. Every reasoning step should be auditable before it reaches you. MiroMind solved this. ā We tried it on a real research task. Evaluate a chip startup across patents, funding, competitors, and technical depth. That kind of work normally takes a week across a dozen tabs. The system got through it in hours, pulling from over 300 sources on its own. It cross-referenced claims across SEC filings, patent databases, and pitch materials. Nobody asked it to find problems. It flagged two contradictions between public filings and investor materials anyway, matching claims across documents that don't look anything alike. That only works because every step is checked before the next one runs. ā Here's how the verification actually works. > Four roles run in sequence. > Planner maps the full reasoning graph. > Executor retrieves and processes data. > ChainChecker validates each inference step. > Verifier confirms outputs against original sources. The reasoning graph is a DAG (directed acyclic graph), a structure where steps flow forward and never loop back on themselves. That means branches run in parallel instead of one at a time. If a branch hits a dead end, the system backtracks to the last valid node and replans from there. Most retrieval pipelines just push through bad inferences. This one actually stops. The point isn't the architecture. The point is that nothing reaches the output without being traced back to a source. ā That traceability is the actual product. Click any conclusion and walk the full chain back to the raw document. Every claim links to where it came from. It also integrates live market data and returns forecasts with actual numbers behind them, not qualitative summaries. Those numbers are traceable too. They market "300 steps to 99% cumulative certainty." The real value isn't the number. It's that every one of those steps is visible. If you can't audit the reasoning, the confidence score is meaningless. This is where the entire industry is heading. The next generation of AI tools won't compete on fluency. They'll compete on verifiability. If verification-first architectures become the standard, the trust model around AI changes completely.