8 AI Critics. Zero Mercy.

Submit your proposal. Face the council. Walk in battle-hardened.

Send any decision to a panel of 8 AI critics — CFO, skeptical engineer, PM devil's advocate, compliance lead, and more. Get back the hardest questions you'll face, your biggest assumption to prove, and exactly how to prep.

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What it is

One proposal.
Eight adversaries.

Most decisions fail not because the idea was bad — but because someone in the room asked a question you weren't ready for. Elenchus eliminates that surprise.

Paste your proposal. In minutes, you have a structured Battle Brief: the CFO's objection, the engineer's failure mode concern, the PM's "where's the evidence?" challenge — and a clear prep plan for each.

8
Distinct expert perspectives on every decision
1
Battle Brief synthesizing what to prep and how
10m
From proposal to fully stress-tested output
4
LLM providers supported — bring your own key
The Council

Eight seats.
Every angle covered.

Not a chatbot that agrees with you. A panel of AI critics each trained to find the specific weakness in their domain — financial, technical, strategic, legal, human.

💰
The CFO
Chief Financial Officer
Challenges: ROI assumptions, cost projections, payback period, total cost of ownership at scale
⚙️
The Skeptical Eng
Principal Engineer
Challenges: Scalability, failure modes, eval gaps, technical debt, what happens when the model is wrong
🔪
The PM Devil
Product Skeptic
Challenges: User evidence, MVP scope, what you're not building, whether the problem is real
🔒
The Compliance Lead
Legal / Security
Challenges: Data privacy, AI liability, auditability, what happens when regulators ask
🥊
The Competitor
Your Smartest Rival's CPO
Challenges: Moat, time-to-copy, strategic differentiation, whether you have a real advantage
🔬
The Researcher
AI Research Scientist
Challenges: Model capabilities vs. reality, eval rigor, hallucination exposure, benchmark validity
🙋
The End User
Actual Human Stakeholder
Challenges: Trust, workflow disruption, adoption friction, whether anyone will actually change how they work
📊
The Exec Sponsor
VP Presenting Upward
Challenges: Narrative clarity, boardroom kill questions, whether this can survive one level up
The output

Your Battle Brief.

battle-brief.md
proposal › Automate compliance review with multi-agent AI...
💰 CFO · Chief Financial Officer
You've claimed $2.5M in savings — but have you modelled total cost of ownership? API costs at scale, maintenance, and retraining are not zero.
What is the payback period if this takes 9 months instead of 6?
2.1s · 847 tokens
⚙️ Skeptical Eng · Principal Engineer
What is your fallback when the agent produces a confident but wrong compliance call? What does the human review step actually look like in practice?
1.8s · 612 tokens
🧠 Battle Brief
Top 3 Hardest Questions
What happens when the AI makes a wrong call and we get fined?
Have you run this on real regulatory documents, or just demos?
What does human sign-off require to be legally defensible?
Biggest Assumption to Prove
4h AI + 2h human review is sufficient for legal sign-off.
Recommended Prep
Pilot on 3 historical compliance changes vs. analyst decisions
Get legal to define what human sign-off legally requires
Model API costs at current volume + 3× growth
$
01

Each council member speaks in their voice

Not generic AI advice. The CFO thinks in payback periods. The engineer thinks in failure modes. Each response is calibrated to that expert's specific blind spot for your domain.

02

The Battle Brief synthesizes everything

After all 8 critics weigh in, you get a structured synthesis: the three hardest questions you'll face, the single most dangerous assumption, and concrete prep actions.

03

Works with any LLM provider

Claude, GPT, Gemini, or a local model via custom endpoint. Bring your own key. No accounts, no subscriptions — run it from your terminal in under 10 minutes.

04

Extendable to your context

Add custom personas for your specific stakeholders. A board member. Your CTO. The investor who always asks about unit economics. The council grows with your use case.

Powerful when it matters

Real decisions.
Real scrutiny.

AI Initiative Proposal
"We should build a multi-agent system to automate our quarterly reporting pipeline. Timeline: 6 months. Engineers: 3."
Skeptical Engineer fires back
"Multi-agent systems don't fail gracefully — they fail silently. What is your observability plan when Agent 3 produces a plausible-looking but wrong output that Agent 4 accepts?"
Product Strategy Decision
"We're pivoting our B2C product to focus exclusively on enterprise. The SMB market isn't monetising fast enough."
The PM Devil fires back
"Enterprise sales cycles are 6–9 months. You have 4 months of runway. What is the specific evidence that enterprise deals will close before SMB revenue would have recovered?"
Budget Request
"I'm requesting $180K to expand our RAG infrastructure from 3M to 50M documents. We need this for the enterprise tier launch."
The CFO fires back
"At what point does retrieval quality degrade as the corpus scales from 3M to 50M? What is your benchmark, and who has validated it on your specific document distribution?"
Team / Org Design
"We want to consolidate our two ML teams — one building models, one deploying them — into a single AI Platform team to move faster."
The End User fires back
"The deployment team exists because model builders don't think about production constraints. If they merge, who owns the on-call rotation when a model degrades at 2am?"
Early Access

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