Scientific Validator: AI Document Integrity Review Tool

Scientific Validator is a prototype for reviewing whether a report, paper, or AI-generated document is structurally trustworthy before a human relies on it.

The system checks documents across multiple integrity layers:

Reasoning structure

Source and citation traceability

Method consistency

Core inquiry continuity

Unsupported or overconfident claims

Possible fabricated or weakly grounded references

Polished language that hides weak logic

The product challenge was to turn a complex validation process into a clear, usable interface. Instead of giving one vague “AI score,” the tool breaks the review into separate report cards so the user can see which parts of the document are sound, which parts need review, and where the reasoning may fail.

My role covered product architecture, validation logic, AI workflow design, UX structure, report framework, and demo narrative.

Key focus areas:

AI document review

Source-grounded validation

Human-in-the-loop review

Research/report integrity

Structured AI outputs

Multi-agent review workflows

Clear UX for complex reasoning checks