Evidence-grounded AI

Evidence-grounded AI for patent intelligence workflows

Evidence-grounded AI means a generated statement can be traced back to the patent evidence and review scope that support it. IPClavis keeps sources, limitations, and unresolved questions attached so a professional can inspect the work instead of trusting a black-box answer.

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How the review workflow works

  1. Define the patent question, evidence scope, and intended review output.
  2. Generate summaries or mappings only from the evidence made available to that workflow.
  3. Check source references, uncertainty, and output boundaries before the work becomes a brief or handoff.

What IPClavis does

  • Keep generated statements close to the patents and passages that support them.
  • Use different evidence and review rules for search, briefs, monitoring, and drafting.
  • Preserve private workspace boundaries while keeping public evidence clearly identified.

What IPClavis does not do

  • Treat a fluent answer as proof without inspectable support.
  • Accept unsupported generated claims as evidence.
  • Treat AI output as a source of legal truth.

How the evidence stays inspectable

  • Review outputs retain the source references used to prepare them.
  • Unsupported statements and missing evidence are treated as review failures, not hidden gaps.
  • The same source-linked boundary applies across reports, FTO briefs, prior-art work, monitoring, and drafts.

Professional review boundary

Evidence-grounded AI improves traceability and review discipline. It does not remove the need for professional evidence review or legal judgment.

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