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Outside-Counsel Memo Cross-Examination Pack for In-House GC

killed [TRIANGULATED] filter 6.5/15 spread ±3.5 signals: 2 independent
What is this?
For in-house General Counsel at 50-500 employee EU/UK companies whose product touches AI Act applicability questions. The GC enters discrete claims from an outside-counsel memo (article cited, classification reached, carve-out invoked, 1-3 sentence reasoning chain) into a structured form. AE runs adversarial multi-model debate against the AI Act text, AI Office FAQs, and ENISA bulletins, then returns named challenge questions in legal vernacular ('the Art. 6(2) carve-out rests on an unstated premise about purpose limitation; please defend or narrow'). The GC sends those challenges back to counsel. The primary graded resolution event is counsel's response within 7-14 days: concession, refinement, or substantive defense — a fast, objective evaluator-side signal that maps directly onto AE's 6-pattern autopsy. Internal pattern names (Fatal Grounding Immunity, Concession Laundering) drive the engine and the GC's confidence ledger but are translated out before contact with counsel. Subsequent regulator publications are a slow secondary signal, not the primary truth path. Recurs on every feature, market, or model change through 2027+.
Why did we consider it?
Under-resourced mid-market EU/UK GCs need a cheap, structured way to cross-examine outside-counsel AI Act memos, and AE's adversarial debate plus 7-14 day counsel-response grading is the rare legal-AI shape with a fast objective signal.
What breaks?
  • The Billable Hour Trap: Sending AI-generated challenges to outside counsel will trigger massive billable hours for the GC, destroying the ROI of the tool.
  • False Ground Truth: A law firm partner's defensive response is subjective ego-protection, not an 'objective reality-graded signal' for the AE engine.
  • Misaligned GC Incentives: GCs are paid to clear roadblocks for company goals, not to delay product launches by playing AI Act trivia with their own hired experts.
Fatal objection: Counsel-reply behavior is a conflict-of-interest signal, not regulatory ground truth, so AE's graded-prediction moat collapses into sentiment classification.
What did we learn?
Killed: fatal_objection_both_confirm.

Filter scores

Five axes, each scored 0-3. Three independent runs by different model perspectives. Median shown.

AxisWhat it measures
data moatDoes this product accumulate proprietary data that compounds?
10x model testDoes a better model make this more valuable, or redundant?
fast feedback loopsCan outputs be graded against reality in <30 days?
solo founder feasibleCan a solo operator build and run this without a team?
AI providers cant eat itDo hyperscalers have structural reasons NOT to build this?
Composite median: 6.5 / 15. Graduation threshold: 9.0. IQR across runs: 3.5.

Evidence

Signal B — Competitor with documented gap

Anthropic's claude-for-legal is a plugin suite for general legal workflows but shows no capability for adversarial multi-model debate against specific regulatory texts (AI Act, AI Office FAQs, ENISA bulletins) or structured cross-examination of outside-counsel memo claims. The gap is the absence of structured adversarial challenge-generation against cited legal provisions and reasoning chains.

Signal D — Demand proxy

{"found":true,"summary":"Weak demand signals: Reddit legal professionals discuss cross-examination preparation workflows (suggesting openness to structured challenge tools), and a legal blog documents 100 practical GenAI prompts for in-house lawyers (confirming in-house counsel are actively adopting AI tooling for legal analysis). No direct discussion of AI Act memo cross-examination was found.","sources":["https://www.reddit.com/r/Lawyertalk/comments/16wli0u/is_it_okay_to_have_a_script_for_cross_examination/","https://tenthings.blog/2025/03/31/ten-things-practical-generative-ai-prompts-for-in…

Evaluation history

WhenStagePhase
2026-05-13 05:48fatal_objectionranked
2026-05-13 05:42fatal_objectionranked
2026-05-13 05:36filter_scorescored
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2026-05-13 05:12evidence_searchargument
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2026-05-10 19:18audience_simulationargument
2026-05-10 19:12red_team_killargument
2026-05-10 19:06steelmanargument
2026-05-10 19:03genesisargument