A recommendation still needs accountable review.

Built from the failure of the first thesis.

Teranode began by testing whether several specialized models could produce a stronger answer than one capable model. That was not the durable result. More models did not make the system a decision-maker.

The useful structure was separation: distinct reviewers could challenge an answer, preserve exceptions, and make the reasoning available to the person who actually decides. Teranode Review keeps that structure and removes the AI committee from the proposition.

Review the recommendation. Record the human judgment.

Teranode records whether the source is an AI model or a human team. For an AI source, its model vendor is excluded. Four review lenses run across at least three model vendors, their attributed findings remain unresolved by any model, and a human disposition is required for every finding. The result is a timestamped review artifact with a human-entered attestation. Reviewer name and role are supplied by the reviewer or firm, not independently verified by Teranode. The SHA-256 fingerprint is a comparison value, not a digital signature.

Teranode is built for decisions where a second look matters. The same control pattern may apply in other settings, but Teranode does not claim legal, medical, or enterprise deployment today.

Built by someone who worked inside the accountability gap.

Dan Zimon has fourteen years of experience across institutional finance and the wealth channel and holds Series 7, Series 66, and insurance licenses. Dan Zimon is a New York state-registered investment adviser representative of Camba Capital, LLC, a New York state-registered investment adviser. The product began with a practical observation: people remain responsible for decisions regardless of whether the analysis begins with AI or with a person.

Teranode is built for the reviewer who must understand what the submitted recommendation may have missed, decide what to do about it, and retain a record of that judgment.

Make the controls inspectable and the limits explicit.

The product discloses source and reviewer provenance, preserves limitations, uses deterministic aggregation and guardrails, and stops before the human decision. The implemented method is on Method; data and security boundaries are on Trust and Security.