Compiled by Citation Safe (Digital Empire Holdings LLC) from the court’s published sanction order (decided August 14, 2026). Quotes and case details come from the order itself — check the primary source before relying on them.

Sanction teardown · CA Indiana, USA · 2026-08-14

Brankle v. Schmell

What happened

In CA Indiana, USA, a filing relied on ChatGPT to help draft legal argument. The court identified the following problems with the citations in that filing:

Fabricated (Legal Norm)

Cited rules that do not exist (inapplicable/nonexistent Commercial Court Rules); court described repeated citations to rules that do not exist.
Fabricated (Case Law)

Cited fictitious case law (repeated references to 'fictitious cases'); court found motion 'riddled with hallucinated authorities.'

Which AI tool

ChatGPT. Note: Charlotin's public database records tool attribution only where a court order, brief, or reporting on the matter states it explicitly; "unidentified" or "implied" means the record indicates AI use but does not name a specific product — we do not guess.

Outcome

Monetary Sanction (upheld) (monetary penalty: 1546 USD.)

Additional detail

The trial court found that Brankle's filings contained repeated citations to rules that do not exist and fictitious cases, stating he appeared to be using ChatGPT to prepare filings. The trial court denied his motion to compel and ordered him to pay Schmell's expenses; the Court of Appeals affirmed, concluding the motion was not substantially justified because it was riddled with hallucinated authorities and that as a pro se litigant he is held to the same standards as trained attorneys.

How Citation Safe would have caught this

Citation Safe runs three deterministic layers before a brief is filed: (1) does the citation exist against CourtListener's database of published opinions, (2) if quoted, does that exact language appear in the source, (3) does the cited case actually support the proposition it is cited for. Fabricated case citations fail Layer 1. Fabricated or misattributed quotations fail Layer 2 even when the underlying case is real. Misrepresented holdings — a real case cited for a proposition it does not support — are the target of Layer 3. None of these checks involve asking another language model whether the citation looks right; they are lookups and text-matches against the actual source, which is why a hallucinated citation has to survive a direct lookup against the authoritative source — not another model's opinion — to earn a VERIFIED stamp; our measured false-verify rate is published live at /quality.

Check a brief before you file it → · See our live false-verify rate

Source: https://www.damiencharlotin.com/documents/2920/Brankle_v._Schmell_USA_14_August_2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).